Systems and methods of autonomously controlling vehicle states

ABSTRACT

Systems and methods for controlling a vehicle are described. A processor of a longitudinal planning system determines a state of the vehicle. The processor determines a state of a leader vehicle. The processor, based on the determined state of the vehicle and the determined state of the leader vehicle, determines a critical distance for the vehicle. The processor compares a distance between the vehicle and the leader vehicle with the critical distance. The processor, based on the comparison, determines whether the vehicle is too close to or too far from the leader vehicle. The processor, based on the determination, applies one or more of overshoot constraints, undershoot constraints, and critical constraints. After applying the one or more of overshoot constraints, undershoot constraints, and critical constraints, the processor determines a target acceleration for the vehicle. The processor controls the vehicle to track the target acceleration for the vehicle.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims the benefits andpriority to U.S. patent application Ser. No. 16/450,691, filed Jun. 24,2019, of the same title, the entire disclosure of which is herebyincorporated by reference, in its entirety, for all that it teaches andfor all purposes.

FIELD

The present disclosure is generally directed to vehicle systems, inparticular, toward autonomously controlling a vehicle.

BACKGROUND

In recent years, transportation methods have changed substantially. Thischange is due in part to a concern over the limited availability ofnatural resources, a proliferation in personal technology, and asocietal shift to adopt more environmentally friendly transportationsolutions. These considerations have encouraged the development of anumber of new flexible-fuel vehicles, hybrid-electric vehicles, andelectric vehicles.

While these vehicles appear to be new, they are generally implemented asa number of traditional subsystems that are merely tied to analternative power source. In fact, the design and construction of thevehicles is limited to standard frame sizes, shapes, materials, andtransportation concepts. Among other things, these limitations fail totake advantage of the benefits of new technology, power sources, andsupport infrastructure.

To perform properly, autonomous vehicles must take into consideration anear infinite number of variables and factors. Contemporary autonomousvehicles, however, are limited by capabilities of memory and processors.An autonomous vehicle navigating through traffic at high speeds is facedwith an ever-increasing number of decisions to make despite anyshortcomings of processing technology.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a vehicle in accordance with embodiments of the presentdisclosure;

FIG. 2 shows a plan view of the vehicle in accordance with at least someembodiments of the present disclosure;

FIG. 3A is a block diagram of an embodiment of a communicationenvironment of the vehicle in accordance with embodiments of the presentdisclosure;

FIG. 3B is a block diagram of an embodiment of interior sensors withinthe vehicle in accordance with embodiments of the present disclosure;

FIG. 3C is a block diagram of an embodiment of a navigation system ofthe vehicle in accordance with embodiments of the present disclosure;

FIG. 4 shows an embodiment of the instrument panel of the vehicleaccording to one embodiment of the present disclosure;

FIG. 5 is a block diagram of an embodiment of a communications subsystemof the vehicle;

FIG. 6 is a block diagram of a computing environment associated with theembodiments presented herein;

FIG. 7 is a block diagram of a computing device associated with one ormore components described herein;

FIG. 8A is an illustration of an environment in accordance with one ormore of the embodiments presented herein;

FIG. 8B is an illustration of a database in accordance with one or moreof the embodiments presented herein;

FIG. 9A is an illustration of a phase plane trajectory graph inaccordance with one or more of the embodiments presented herein;

FIG. 9B is an illustration of a phase plane trajectory graph inaccordance with one or more of the embodiments presented herein;

FIG. 9C is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9D is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9E is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9F is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9G is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9H is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9I is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9J is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9K is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 9L is an illustration of a phase portrait in accordance with one ormore of the embodiments presented herein;

FIG. 10A is an illustration of a contour plot in accordance with one ormore of the embodiments presented herein;

FIG. 10B is an illustration of a contour plot in accordance with one ormore of the embodiments presented herein;

FIG. 11 is a flow chart of an embodiment of a method of a control systemin accordance with one or more of the embodiments presented herein.

DETAILED DESCRIPTION

Embodiments of the present disclosure will be described in connectionwith a vehicle, and in some embodiments, an electric vehicle,rechargeable electric vehicle, and/or hybrid-electric vehicle andassociated systems.

FIG. 1 shows a perspective view of a vehicle 100 in accordance withembodiments of the present disclosure. The electric vehicle 100comprises a vehicle front 110, vehicle aft or rear 120, vehicle roof130, at least one vehicle side 160, a vehicle undercarriage 140, and avehicle interior 150. In any event, the vehicle 100 may include a frame104 and one or more body panels 108 mounted or affixed thereto. Thevehicle 100 may include one or more interior components (e.g.,components inside an interior space 150, or user space, of a vehicle100, etc.), exterior components (e.g., components outside of theinterior space 150, or user space, of a vehicle 100, etc.), drivesystems, controls systems, structural components, etc.

Although shown in the form of a car, it should be appreciated that thevehicle 100 described herein may include any conveyance or model of aconveyance, where the conveyance was designed for the purpose of movingone or more tangible objects, such as people, animals, cargo, and thelike. The term vehicle does not require that a conveyance moves or iscapable of movement. Typical vehicles may include but are in no waylimited to cars, trucks, motorcycles, busses, automobiles, trains,railed conveyances, boats, ships, marine conveyances, submarineconveyances, airplanes, space craft, flying machines, human-poweredconveyances, and the like.

In some embodiments, the vehicle 100 may include a number of sensors,devices, and/or systems that are capable of assisting in drivingoperations, e.g., autonomous or semi-autonomous control. Examples of thevarious sensors and systems may include, but are in no way limited to,one or more of cameras (e.g., independent, stereo, combined image,etc.), infrared (IR) sensors, radio frequency (RF) sensors, ultrasonicsensors (e.g., transducers, transceivers, etc.), RADAR sensors (e.g.,object-detection sensors and/or systems), LIDAR (Light Imaging,Detection, And Ranging) systems, odometry sensors and/or devices (e.g.,encoders, etc.), orientation sensors (e.g., accelerometers, gyroscopes,magnetometer, etc.), navigation sensors and systems (e.g., GPS, etc.),and other ranging, imaging, and/or object-detecting sensors. The sensorsmay be disposed in an interior space 150 of the vehicle 100 and/or on anoutside of the vehicle 100. In some embodiments, the sensors and systemsmay be disposed in one or more portions of a vehicle 100 (e.g., theframe 104, a body panel, a compartment, etc.).

The vehicle sensors and systems may be selected and/or configured tosuit a level of operation associated with the vehicle 100. Among otherthings, the number of sensors used in a system may be altered toincrease or decrease information available to a longitudinal planningsystem (e.g., affecting control capabilities of the vehicle 100).Additionally, or alternatively, the sensors and systems may be part ofone or more advanced driver assistance systems (ADAS) associated with avehicle 100. In any event, the sensors and systems may be used toprovide driving assistance at any level of operation (e.g., fromfully-manual to fully-autonomous operations, etc.) as described herein.

The various levels of vehicle control and/or operation can be describedas corresponding to a level of autonomy associated with a vehicle 100for vehicle driving operations. For instance, at Level 0, orfully-manual driving operations, a driver (e.g., a human driver) may beresponsible for all the driving control operations (e.g., steering,accelerating, braking, etc.) associated with the vehicle. Level 0 may bereferred to as a “No Automation” level. At Level 1, the vehicle may beresponsible for a limited number of the driving operations associatedwith the vehicle, while the driver is still responsible for most drivingcontrol operations. An example of a Level 1 vehicle may include avehicle in which the throttle control and/or braking operations may becontrolled by the vehicle (e.g., cruise control operations, etc.). Level1 may be referred to as a “Driver Assistance” level. At Level 2, thevehicle may collect information (e.g., via one or more drivingassistance systems, sensors, etc.) about an environment of the vehicle(e.g., surrounding area, roadway, traffic, ambient conditions, etc.) anduse the collected information to control driving operations (e.g.,steering, accelerating, braking, etc.) associated with the vehicle. In aLevel 2 autonomous vehicle, the driver may be required to perform otheraspects of driving operations not controlled by the vehicle. Level 2 maybe referred to as a “Partial Automation” level. It should be appreciatedthat Levels 0-2 all involve the driver monitoring the driving operationsof the vehicle.

At Level 3, the driver may be separated from controlling all the drivingoperations of the vehicle except when the vehicle makes a request forthe operator to act or intervene in controlling one or more drivingoperations. In other words, the driver may be separated from controllingthe vehicle unless the driver is required to take over for the vehicle.Level 3 may be referred to as a “Conditional Automation” level. At Level4, the driver may be separated from controlling all the drivingoperations of the vehicle and the vehicle may control driving operationseven when a user fails to respond to a request to intervene. Level 4 maybe referred to as a “High Automation” level. At Level 5, the vehicle cancontrol all the driving operations associated with the vehicle in alldriving modes. The vehicle in Level 5 may continually monitor traffic,vehicular, roadway, and/or environmental conditions while driving thevehicle. In Level 5, there is no human driver interaction required inany driving mode. Accordingly, Level 5 may be referred to as a “FullAutomation” level. It should be appreciated that in Levels 3-5 thevehicle, and/or one or more automated driving systems associated withthe vehicle, monitors the driving operations of the vehicle and thedriving environment.

As shown in FIG. 1 , the vehicle 100 may, for example, include at leastone of a ranging and imaging system 112 (e.g., LIDAR, etc.), an imagingsensor 116A, 116F (e.g., camera, IR, etc.), a radio object-detection andranging system sensors 116B (e.g., RADAR, RF, etc.), ultrasonic sensors116C, and/or other object-detection sensors 116D, 116E. In someembodiments, the LIDAR system 112 and/or sensors may be mounted on aroof 130 of the vehicle 100. In one embodiment, the RADAR sensors 116Bmay be disposed at least at a front 110, aft 120, or side 160 of thevehicle 100. Among other things, the RADAR sensors may be used tomonitor and/or detect a position of other vehicles, pedestrians, and/orother objects near, or proximal to, the vehicle 100. While shownassociated with one or more areas of a vehicle 100, it should beappreciated that any of the sensors and systems 116A-K, 112 illustratedin FIGS. 1 and 2 may be disposed in, on, and/or about the vehicle 100 inany position, area, and/or zone of the vehicle 100.

Referring now to FIG. 2 , a plan view of a vehicle 100 will be describedin accordance with embodiments of the present disclosure. In particular,FIG. 2 shows a vehicle sensing environment 200 at least partiallydefined by the sensors and systems 116A-K, 112 disposed in, on, and/orabout the vehicle 100. Each sensor 116A-K may include an operationaldetection range R and operational detection angle. The operationaldetection range R may define the effective detection limit, or distance,of the sensor 116A-K. In some cases, this effective detection limit maybe defined as a distance from a portion of the sensor 116A-K (e.g., alens, sensing surface, etc.) to a point in space offset from the sensor116A-K. The effective detection limit may define a distance, beyondwhich, the sensing capabilities of the sensor 116A-K deteriorate, failto work, or are unreliable. In some embodiments, the effective detectionlimit may define a distance, within which, the sensing capabilities ofthe sensor 116A-K are able to provide accurate and/or reliable detectioninformation. The operational detection angle may define at least oneangle of a span, or between horizontal and/or vertical limits, of asensor 116A-K. As can be appreciated, the operational detection limitand the operational detection angle of a sensor 116A-K together maydefine the effective detection zone 216A-D (e.g., the effectivedetection area, and/or volume, etc.) of a sensor 116A-K.

In some embodiments, the vehicle 100 may include a ranging and imagingsystem 112 such as LIDAR, or the like. The ranging and imaging system112 may be configured to detect visual information in an environmentsurrounding the vehicle 100. The visual information detected in theenvironment surrounding the ranging and imaging system 112 may beprocessed (e.g., via one or more sensor and/or system processors, etc.)to generate a complete 360-degree view of an environment 200 around thevehicle. The ranging and imaging system 112 may be configured togenerate changing 360-degree views of the environment 200 in real-time,for instance, as the vehicle 100 drives. In some cases, the ranging andimaging system 112 may have an effective detection limit 204 that issome distance from the center of the vehicle 100 outward over 360degrees. The effective detection limit 204 of the ranging and imagingsystem 112 defines a view zone 208 (e.g., an area and/or volume, etc.)surrounding the vehicle 100. Any object falling outside of the view zone208 is in the undetected zone 212 and would not be detected by theranging and imaging system 112 of the vehicle 100.

Sensor data and information may be collected by one or more sensors orsystems 116A-K, 112 of the vehicle 100 monitoring the vehicle sensingenvironment 200. This information may be processed (e.g., via aprocessor, computer-vision system, etc.) to determine targets (e.g.,objects, signs, people, markings, roadways, conditions, etc.) inside oneor more detection zones 208, 216A-D associated with the vehicle sensingenvironment 200. In some cases, information from multiple sensors 116A-Kmay be processed to form composite sensor detection information. Forexample, a first sensor 116A and a second sensor 116F may correspond toa first camera 116A and a second camera 116F aimed in a forwardtraveling direction of the vehicle 100. In this example, imagescollected by the cameras 116A, 116F may be combined to form stereo imageinformation. This composite information may increase the capabilities ofa single sensor in the one or more sensors 116A-K by, for example,adding the ability to determine depth associated with targets in the oneor more detection zones 208, 216A-D. Similar image data may be collectedby rear view cameras (e.g., sensors 116G, 116H) aimed in a rearwardtraveling direction vehicle 100.

In some embodiments, multiple sensors 116A-K may be effectively joinedto increase a sensing zone and provide increased sensing coverage. Forinstance, multiple RADAR sensors 116B disposed on the front 110 of thevehicle may be joined to provide a zone 216B of coverage that spansacross an entirety of the front 110 of the vehicle. In some cases, themultiple RADAR sensors 116B may cover a detection zone 216B thatincludes one or more other sensor detection zones 216A. Theseoverlapping detection zones may provide redundant sensing, enhancedsensing, and/or provide greater detail in sensing within a particularportion (e.g., zone 216A) of a larger zone (e.g., zone 216B).Additionally, or alternatively, the sensors 116A-K of the vehicle 100may be arranged to create a complete coverage, via one or more sensingzones 208, 216A-D around the vehicle 100. In some areas, the sensingzones 216C of two or more sensors 116D, 116E may intersect at an overlapzone 220. In some areas, the angle and/or detection limit of two or moresensing zones 216C, 216D (e.g., of two or more sensors 116E, 1167, 116K)may meet at a virtual intersection point 224.

The vehicle 100 may include a number of sensors 116E, 116G, 116H, 1167,116K disposed proximal to the rear 120 of the vehicle 100. These sensorscan include, but are in no way limited to, an imaging sensor, camera,IR, a radio object-detection and ranging sensors, RADAR, RF, ultrasonicsensors, and/or other object-detection sensors. Among other things,these sensors 116E, 116G, 116H, 116J, 116K may detect targets near orapproaching the rear of the vehicle 100. For example, another vehicleapproaching the rear 120 of the vehicle 100 may be detected by one ormore of the ranging and imaging system (e.g., LIDAR) 112, rear-viewcameras 116G, 116H, and/or rear facing RADAR sensors 116J, 116K. Asdescribed above, the images from the rear-view cameras 116G, 116H may beprocessed to generate a stereo view (e.g., providing depth associatedwith an object or environment, etc.) for targets visible to both cameras116G, 116H. As another example, the vehicle 100 may be driving and oneor more of the ranging and imaging system 112, front-facing cameras116A, 116F, front-facing RADAR sensors 116B, and/or ultrasonic sensors116C may detect targets in front of the vehicle 100. This approach mayprovide critical sensor information to a longitudinal planning system inat least one of the autonomous driving levels described above. Forinstance, when the vehicle 100 is driving autonomously (e.g., Level 3,Level 4, or Level 5) and detects other vehicles stopped in a travelpath, the sensor detection information may be sent to the longitudinalplanning system of the vehicle 100 to control a driving operation (e.g.,braking, decelerating, etc.) associated with the vehicle 100 (in thisexample, slowing the vehicle 100 as to avoid colliding with the stoppedother vehicles). As yet another example, the vehicle 100 may beoperating and one or more of the ranging and imaging system 112, and/orthe side-facing sensors 116D, 116E (e.g., RADAR, ultrasonic, camera,combinations thereof, and/or other type of sensor), may detect targetsat a side of the vehicle 100. It should be appreciated that the sensors116A-K may detect a target that is both at a side 160 and a front 110 ofthe vehicle 100 (e.g., disposed at a diagonal angle to a centerline ofthe vehicle 100 running from the front 110 of the vehicle 100 to therear 120 of the vehicle). Additionally, or alternatively, the sensors116A-K may detect a target that is both, or simultaneously, at a side160 and a rear 120 of the vehicle 100 (e.g., disposed at a diagonalangle to the centerline of the vehicle 100).

FIGS. 3A-3C are block diagrams of an embodiment of a communicationenvironment 300 of the vehicle 100 in accordance with embodiments of thepresent disclosure. The communication system 300 may include one or morevehicle driving vehicle sensors and systems 304, sensor processors 340,sensor data memory 344, longitudinal planning system 348, communicationssubsystem 350, control data 364, computing devices 368, display devices372, and other components 374 that may be associated with a vehicle 100.These associated components may be electrically and/or communicativelycoupled to one another via at least one bus 360. In some embodiments,the one or more associated components may send and/or receive signalsacross a communication network 352 to at least one of a navigationsource 356A, a control source 356B, or some other entity 356N.

In accordance with at least some embodiments of the present disclosure,the communication network 352 may comprise any type of knowncommunication medium or collection of communication media and may useany type of protocols, such as SIP, TCP/IP, SNA, IPX, AppleTalk, and thelike, to transport messages between endpoints. The communication network352 may include wired and/or wireless communication technologies. TheInternet is an example of the communication network 352 that constitutesan Internet Protocol (IP) network consisting of many computers,computing networks, and other communication devices located all over theworld, which are connected through many telephone systems and othermeans. Other examples of the communication network 352 include, withoutlimitation, a standard Plain Old Telephone System (POTS), an IntegratedServices Digital Network (ISDN), the Public Switched Telephone Network(PSTN), a Local Area Network (LAN), such as an Ethernet network, aToken-Ring network and/or the like, a Wide Area Network (WAN), a virtualnetwork, including without limitation a virtual private network (“VPN”);the Internet, an intranet, an extranet, a cellular network, an infra-rednetwork; a wireless network (e.g., a network operating under any of theIEEE 802.9 suite of protocols, the Bluetooth® protocol known in the art,and/or any other wireless protocol), and any other type ofpacket-switched or circuit-switched network known in the art and/or anycombination of these and/or other networks. In addition, it can beappreciated that the communication network 352 need not be limited toany one network type, and instead may be comprised of a number ofdifferent networks and/or network types. The communication network 352may comprise a number of different communication media such as coaxialcable, copper cable/wire, fiber-optic cable, antennas fortransmitting/receiving wireless messages, and combinations thereof.

The driving vehicle sensors and systems 304 may include at least onenavigation 308 (e.g., global positioning system (GPS), etc.),orientation 312, odometry 316, LIDAR 320, RADAR 324, ultrasonic 328,camera 332, infrared (IR) 336, and/or other sensor or system 338. Thesedriving vehicle sensors and systems 304 may be similar, if notidentical, to the sensors and systems 116A-K, 112 described inconjunction with FIGS. 1 and 2 .

The navigation sensor 308 may include one or more sensors havingreceivers and antennas that are configured to utilize a satellite-basednavigation system including a network of navigation satellites capableof providing geolocation and time information to at least one componentof the vehicle 100. Examples of the navigation sensor 308 as describedherein may include, but are not limited to, at least one of Garmin® GLO™family of GPS and GLONASS combination sensors, Garmin® GPS 15x™ familyof sensors, Garmin® GPS 16x™ family of sensors with high-sensitivityreceiver and antenna, Garmin® GPS 18x OEM family of high-sensitivity GPSsensors, Dewetron DEWE-VGPS series of GPS sensors, GlobalSat 1-Hz seriesof GPS sensors, other industry-equivalent navigation sensors and/orsystems, and may perform navigational and/or geolocation functions usingany known or future-developed standard and/or architecture.

The orientation sensor 312 may include one or more sensors configured todetermine an orientation of the vehicle 100 relative to at least onereference point. In some embodiments, the orientation sensor 312 mayinclude at least one pressure transducer, stress/strain gauge,accelerometer, gyroscope, and/or geomagnetic sensor. Examples of thenavigation sensor 308 as described herein may include, but are notlimited to, at least one of Bosch Sensortec BMX 160 series low-powerabsolute orientation sensors, Bosch Sensortec BMX055 9-axis sensors,Bosch Sensortec BMI055 6-axis inertial sensors, Bosch Sensortec BMI1606-axis inertial sensors, Bosch Sensortec BMF055 9-axis inertial sensors(accelerometer, gyroscope, and magnetometer) with integrated Cortex M0+microcontroller, Bosch Sensortec BMP280 absolute barometric pressuresensors, Infineon TLV493D-A1B6 3D magnetic sensors, InfineonTLI493D-W1B6 3D magnetic sensors, Infineon TL family of 3D magneticsensors, Murata Electronics SCC2000 series combined gyro sensor andaccelerometer, Murata Electronics SCC1300 series combined gyro sensorand accelerometer, other industry-equivalent orientation sensors and/orsystems, which may perform orientation detection and/or determinationfunctions using any known or future-developed standard and/orarchitecture.

The odometry sensor and/or system 316 may include one or more componentsthat is configured to determine a change in position of the vehicle 100over time. In some embodiments, the odometry system 316 may utilize datafrom one or more other sensors and/or systems 304 in determining aposition (e.g., distance, location, etc.) of the vehicle 100 relative toa previously measured position for the vehicle 100. Additionally, oralternatively, the odometry sensors 316 may include one or moreencoders, Hall speed sensors, and/or other measurement sensors/devicesconfigured to measure a wheel speed, rotation, and/or number ofrevolutions made over time. Examples of the odometry sensor/system 316as described herein may include, but are not limited to, at least one ofInfineon TLE4924/26/27/28C high-performance speed sensors, InfineonTL4941plusC(B) single chip differential Hall wheel-speed sensors,Infineon TL5041plusC Giant Magnetoresistance (GMR) effect sensors,Infineon TL family of magnetic sensors, EPC Model 25SP Accu-CoderPro™incremental shaft encoders, EPC Model 30M compact incremental encoderswith advanced magnetic sensing and signal processing technology, EPCModel 925 absolute shaft encoders, EPC Model 958 absolute shaftencoders, EPC Model MA36S/MA63S/SA36S absolute shaft encoders, Dynapar™F18 commutating optical encoder, Dynapar™ HS35R family of phased arrayencoder sensors, other industry-equivalent odometry sensors and/orsystems, and may perform change in position detection and/ordetermination functions using any known or future-developed standardand/or architecture.

The LIDAR sensor/system 320 may include one or more componentsconfigured to measure distances to targets using laser illumination. Insome embodiments, the LIDAR sensor/system 320 may provide 3D imagingdata of an environment around the vehicle 100. The imaging data may beprocessed to generate a full 360-degree view of the environment aroundthe vehicle 100. The LIDAR sensor/system 320 may include a laser lightgenerator configured to generate a plurality of target illuminationlaser beams (e.g., laser light channels). In some embodiments, thisplurality of laser beams may be aimed at, or directed to, a rotatingreflective surface (e.g., a mirror) and guided outwardly from the LIDARsensor/system 320 into a measurement environment. The rotatingreflective surface may be configured to continually rotate 360 degreesabout an axis, such that the plurality of laser beams is directed in afull 360-degree range around the vehicle 100. A photodiode receiver ofthe LIDAR sensor/system 320 may detect when light from the plurality oflaser beams emitted into the measurement environment returns (e.g.,reflected echo) to the LIDAR sensor/system 320. The LIDAR sensor/system320 may calculate, based on a time associated with the emission of lightto the detected return of light, a distance from the vehicle 100 to theilluminated target. In some embodiments, the LIDAR sensor/system 320 maygenerate over 2.0 million points per second and have an effectiveoperational range of at least 100 meters. Examples of the LIDARsensor/system 320 as described herein may include, but are not limitedto, at least one of Velodyne® LiDAR™ HDL-64E 64-channel LIDAR sensors,Velodyne® LiDAR™ HDL-32E 32-channel LIDAR sensors, Velodyne® LiDAR™PUCK™ VLP-16 16-channel LIDAR sensors, Leica Geosystems Pegasus: Twomobile sensor platform, Garmin® LIDAR-Lite v3 measurement sensor,Quanergy M8 LiDAR sensors, Quanergy S3 solid state LiDAR sensor,LeddarTech® LeddarVU compact solid state fixed-beam LIDAR sensors, otherindustry-equivalent LIDAR sensors and/or systems, and may performilluminated target and/or obstacle detection in an environment aroundthe vehicle 100 using any known or future-developed standard and/orarchitecture.

The RADAR sensors 324 may include one or more radio components that areconfigured to detect objects/targets in an environment of the vehicle100. In some embodiments, the RADAR sensors 324 may determine adistance, position, and/or movement vector (e.g., angle, speed, etc.)associated with a target over time. The RADAR sensors 324 may include atransmitter configured to generate and emit electromagnetic waves (e.g.,radio, microwaves, etc.) and a receiver configured to detect returnedelectromagnetic waves. In some embodiments, the RADAR sensors 324 mayinclude at least one processor configured to interpret the returnedelectromagnetic waves and determine locational properties of targets.Examples of the RADAR sensors 324 as described herein may include, butare not limited to, at least one of Infineon RASIC™ RTN7735PLtransmitter and RRN7745PL/46PL receiver sensors, Autoliv ASP VehicleRADAR sensors, Delphi L2C0051TR 77 GHz ESR Electronically Scanning Radarsensors, Fujitsu Ten Ltd. Automotive Compact 77 GHz 3D Electronic ScanMillimeter Wave Radar sensors, other industry-equivalent RADAR sensorsand/or systems, and may perform radio target and/or obstacle detectionin an environment around the vehicle 100 using any known orfuture-developed standard and/or architecture.

The ultrasonic sensors 328 may include one or more components that areconfigured to detect objects/targets in an environment of the vehicle100. In some embodiments, the ultrasonic sensors 328 may determine adistance, position, and/or movement vector (e.g., angle, speed, etc.)associated with a target over time. The ultrasonic sensors 328 mayinclude an ultrasonic transmitter and receiver, or transceiver,configured to generate and emit ultrasound waves and interpret returnedechoes of those waves. In some embodiments, the ultrasonic sensors 328may include at least one processor configured to interpret the returnedultrasonic waves and determine locational properties of targets.Examples of the ultrasonic sensors 328 as described herein may include,but are not limited to, at least one of Texas Instruments TIDA-00151automotive ultrasonic sensor interface IC sensors, MaxBotix® MB8450ultrasonic proximity sensor, MaxBotix® ParkSonar™-EZ ultrasonicproximity sensors, Murata Electronics MA40H1S-R open-structureultrasonic sensors, Murata Electronics MA40S4R/S open-structureultrasonic sensors, Murata Electronics MA58MF14-7N waterproof ultrasonicsensors, other industry-equivalent ultrasonic sensors and/or systems,and may perform ultrasonic target and/or obstacle detection in anenvironment around the vehicle 100 using any known or future-developedstandard and/or architecture.

The camera sensors 332 may include one or more components configured todetect image information associated with an environment of the vehicle100. In some embodiments, the camera sensors 332 may include a lens,filter, image sensor, and/or a digital image processer. It is an aspectof the present disclosure that multiple camera sensors 332 may be usedtogether to generate stereo images providing depth measurements.Examples of the camera sensors 332 as described herein may include, butare not limited to, at least one of ON Semiconductor® MT9V024 GlobalShutter VGA GS CMOS image sensors, Teledyne DALSA Falcon2 camerasensors, CMOSIS CMV50000 high-speed CMOS image sensors, otherindustry-equivalent camera sensors and/or systems, and may performvisual target and/or obstacle detection in an environment around thevehicle 100 using any known or future-developed standard and/orarchitecture.

The infrared (IR) sensors 336 may include one or more componentsconfigured to detect image information associated with an environment ofthe vehicle 100. The IR sensors 336 may be configured to detect targetsin low-light, dark, or poorly-lit environments. The IR sensors 336 mayinclude an IR light emitting element (e.g., IR light emitting diode(LED), etc.) and an IR photodiode. In some embodiments, the IRphotodiode may be configured to detect returned IR light at or about thesame wavelength to that emitted by the IR light emitting element. Insome embodiments, the IR sensors 336 may include at least one processorconfigured to interpret the returned IR light and determine locationalproperties of targets. The IR sensors 336 may be configured to detectand/or measure a temperature associated with a target (e.g., an object,pedestrian, other vehicle, etc.). Examples of IR sensors 336 asdescribed herein may include, but are not limited to, at least one ofOpto Diode lead-salt IR array sensors, Opto Diode OD-850 Near-IR LEDsensors, Opto Diode SA/SHA727 steady state IR emitters and IR detectors,FLIR® LS microbolometer sensors, FLIR® TacFLIR 380-HD InSb MWIR FPA andHD MWIR thermal sensors, FLIR® VOx 640×480 pixel detector sensors,Delphi IR sensors, other industry-equivalent IR sensors and/or systems,and may perform IR visual target and/or obstacle detection in anenvironment around the vehicle 100 using any known or future-developedstandard and/or architecture.

The vehicle 100 can also include one or more interior sensors 337.Interior sensors 337 can measure characteristics of the insideenvironment of the vehicle 100. The interior sensors 337 may be asdescribed in conjunction with FIG. 3B.

A navigation system 302 can include any hardware and/or software used tonavigate the vehicle either manually or autonomously. The navigationsystem 302 may be as described in conjunction with FIG. 3C.

In some embodiments, the driving vehicle sensors and systems 304 mayinclude other sensors 338 and/or combinations of the sensors 306-337described above. Additionally, or alternatively, one or more of thesensors 306-337 described above may include one or more processorsconfigured to process and/or interpret signals detected by the one ormore sensors 306-337. In some embodiments, the processing of at leastsome sensor information provided by the vehicle sensors and systems 304may be processed by at least one sensor processor 340. Raw and/orprocessed sensor data may be stored in a sensor data memory 344 storagemedium. In some embodiments, the sensor data memory 344 may storeinstructions used by the sensor processor 340 for processing sensorinformation provided by the sensors and systems 304. In any event, thesensor data memory 344 may be a disk drive, optical storage device,solid-state storage device such as a random-access memory (“RAM”) and/ora read-only memory (“ROM”), which can be programmable, flash-updateable,and/or the like.

The longitudinal planning system 348 may receive processed sensorinformation from the sensor processor 340 and determine to control anaspect of the vehicle 100. Controlling an aspect of the vehicle 100 mayinclude presenting information via one or more display devices 372associated with the vehicle, sending commands to one or more computingdevices 368 associated with the vehicle, and/or controlling a drivingoperation of the vehicle. In some embodiments, the longitudinal planningsystem 348 may correspond to one or more computing systems that controldriving operations of the vehicle 100 in accordance with the Levels ofdriving autonomy described above. In one embodiment, the longitudinalplanning system 348 may operate a speed of the vehicle 100 bycontrolling an output signal to the accelerator and/or braking system ofthe vehicle. In this example, the longitudinal planning system 348 mayreceive sensor data describing an environment surrounding the vehicle100 and, based on the sensor data received, determine to adjust theacceleration, power output, and/or braking of the vehicle 100. Thelongitudinal planning system 348 may additionally control steeringand/or other driving functions of the vehicle 100.

The longitudinal planning system 348 may communicate, in real-time, withthe driving sensors and systems 304 forming a feedback loop. Inparticular, upon receiving sensor information describing a condition oftargets in the environment surrounding the vehicle 100, the longitudinalplanning system 348 may autonomously make changes to a driving operationof the vehicle 100. The longitudinal planning system 348 may thenreceive subsequent sensor information describing any change to thecondition of the targets detected in the environment as a result of thechanges made to the driving operation. This continual cycle ofobservation (e.g., via the sensors, etc.) and action (e.g., selectedcontrol or non-control of vehicle operations, etc.) allows the vehicle100 to operate autonomously in the environment.

In some embodiments, the one or more components of the vehicle 100(e.g., the driving vehicle sensors 304, longitudinal planning system348, display devices 372, etc.) may communicate across the communicationnetwork 352 to one or more entities 356A-N via a communicationssubsystem 350 of the vehicle 100. Embodiments of the communicationssubsystem 350 are described in greater detail in conjunction with FIG. 5. For instance, the navigation sensors 308 may receive globalpositioning, location, and/or navigational information from a navigationsource 356A. In some embodiments, the navigation source 356A may be aglobal navigation satellite system (GNSS) similar, if not identical, toNAVSTAR GPS, GLONASS, EU Galileo, and/or the BeiDou Navigation SatelliteSystem (BDS) to name a few.

In some embodiments, the longitudinal planning system 348 may receivecontrol information from one or more control sources 356B. The controlsource 356 may provide vehicle control information including autonomousdriving control commands, vehicle operation override control commands,and the like. The control source 356 may correspond to an autonomouslongitudinal planning system, a traffic control system, anadministrative control entity, and/or some other controlling server. Itis an aspect of the present disclosure that the longitudinal planningsystem 348 and/or other components of the vehicle 100 may exchangecommunications with the control source 356 across the communicationnetwork 352 and via the communications subsystem 350.

Information associated with controlling driving operations of thevehicle 100 may be stored in a control data memory 364 storage medium.The control data memory 364 may store instructions used by thelongitudinal planning system 348 for controlling driving operations ofthe vehicle 100, historical control information, autonomous drivingcontrol rules, and the like. In some embodiments, the control datamemory 364 may be a disk drive, optical storage device, solid-statestorage device such as a random-access memory (“RAM”) and/or a read-onlymemory (“ROM”), which can be programmable, flash-updateable, and/or thelike.

In addition to the mechanical components described herein, the vehicle100 may include a number of user interface devices. The user interfacedevices receive and translate human input into a mechanical movement orelectrical signal or stimulus. The human input may be one or more ofmotion (e.g., body movement, body part movement, in two-dimensional orthree-dimensional space, etc.), voice, touch, and/or physicalinteraction with the components of the vehicle 100. In some embodiments,the human input may be configured to control one or more functions ofthe vehicle 100 and/or systems of the vehicle 100 described herein. Userinterfaces may include, but are in no way limited to, at least onegraphical user interface of a display device, steering wheel ormechanism, transmission lever or button (e.g., including park, neutral,reverse, and/or drive positions, etc.), throttle control pedal ormechanism, brake control pedal or mechanism, power control switch,communications equipment, etc.

FIG. 3B shows a block diagram of an embodiment of interior sensors 337for a vehicle 100. The interior sensors 337 may be arranged into one ormore groups, based at least partially on the function of the interiorsensors 337. For example, the interior space of a vehicle 100 mayinclude environmental sensors, user interface sensor(s), and/or safetysensors. Additionally, or alternatively, there may be sensors associatedwith various devices inside the vehicle (e.g., smart phones, tablets,mobile computers, wearables, etc.)

Environmental sensors may comprise sensors configured to collect datarelating to the internal environment of a vehicle 100. Examples ofenvironmental sensors may include one or more of but are not limited to:oxygen/air sensors 301, temperature sensors 303, humidity sensors 305,light/photo sensors 307, and more. The oxygen/air sensors 301 may beconfigured to detect a quality or characteristic of the air in theinterior space 108 of the vehicle 100 (e.g., ratios and/or types ofgasses comprising the air inside the vehicle 100, dangerous gas levels,safe gas levels, etc.). Temperature sensors 303 may be configured todetect temperature readings of one or more objects, users 216, and/orareas of a vehicle 100. Humidity sensors 305 may detect an amount ofwater vapor present in the air inside the vehicle 100. The light/photosensors 307 can detect an amount of light present in the vehicle 100.Further, the light/photo sensors 307 may be configured to detect variouslevels of light intensity associated with light in the vehicle 100.

User interface sensors may comprise sensors configured to collect datarelating to one or more users (e.g., a driver and/or passenger(s)) in avehicle 100. As can be appreciated, the user interface sensors mayinclude sensors that are configured to collect data from users 216 inone or more areas of the vehicle 100. Examples of user interface sensorsmay include one or more of, but are not limited to: infrared sensors309, motion sensors 311, weight sensors 313, wireless network sensors315, biometric sensors 317, camera (or image) sensors 319, audio sensors321, and more.

Infrared sensors 309 may be used to measure IR light irradiating from atleast one surface, user, or another object in the vehicle 100. Amongother things, the infrared sensors 309 may be used to measuretemperatures, form images (especially in low light conditions), identifyusers 216, and even detect motion in the vehicle 100.

The motion sensors 311 may detect motion and/or movement of objectsinside the vehicle 100. Optionally, the motion sensors 311 may be usedalone or in combination to detect movement. For example, a user may beoperating a vehicle 100 (e.g., while driving, etc.) when a passenger inthe rear of the vehicle 100 unbuckles a safety belt and proceeds to moveabout the vehicle 10. In this example, the movement of the passengercould be detected by the motion sensors 311. In response to detectingthe movement and/or the direction associated with the movement, thepassenger may be prevented from interfacing with and/or accessing atleast some of the vehicle control features. As can be appreciated, theuser may be alerted of the movement/motion such that the user can act toprevent the passenger from interfering with the vehicle controls.Optionally, the number of motion sensors in a vehicle may be increasedto increase an accuracy associated with motion detected in the vehicle100.

Weight sensors 313 may be employed to collect data relating to objectsand/or users in various areas of the vehicle 100. In some cases, theweight sensors 313 may be included in the seats and/or floor of avehicle 100. Optionally, the vehicle 100 may include a wireless networksensor 315. This sensor 315 may be configured to detect one or morewireless network(s) inside the vehicle 100. Examples of wirelessnetworks may include, but are not limited to, wireless communicationsutilizing Bluetooth®, Wi-Fi™, ZigBee, IEEE 802.11, and other wirelesstechnology standards. For example, a mobile hotspot may be detectedinside the vehicle 100 via the wireless network sensor 315. In thiscase, the vehicle 100 may determine to utilize and/or share the mobilehotspot detected via/with one or more other devices associated with thevehicle 100.

Biometric sensors 317 may be employed to identify and/or recordcharacteristics associated with a user. It is anticipated that biometricsensors 317 can include at least one of image sensors, IR sensors,fingerprint readers, weight sensors, load cells, force transducers,heart rate monitors, blood pressure monitors, and the like as providedherein.

The camera sensors 319 may record still images, video, and/orcombinations thereof. Camera sensors 319 may be used alone or incombination to identify objects, users, and/or other features, insidethe vehicle 100. Two or more camera sensors 319 may be used incombination to form, among other things, stereo and/or three-dimensional(3D) images. The stereo images can be recorded and/or used to determinedepth associated with objects and/or users in a vehicle 100. Further,the camera sensors 319 used in combination may determine the complexgeometry associated with identifying characteristics of a user. Forexample, the camera sensors 319 may be used to determine dimensionsbetween various features of a user's face (e.g., the depth/distance froma user's nose to a user's cheeks, a linear distance between the centerof a user's eyes, and more). These dimensions may be used to verify,record, and even modify characteristics that serve to identify a user.The camera sensors 319 may also be used to determine movement associatedwith objects and/or users within the vehicle 100. It should beappreciated that the number of image sensors used in a vehicle 100 maybe increased to provide greater dimensional accuracy and/or views of adetected image in the vehicle 100.

The audio sensors 321 may be configured to receive audio input from auser of the vehicle 100. The audio input from a user may correspond tovoice commands, conversations detected in the vehicle 100, phone callsmade in the vehicle 100, and/or other audible expressions made in thevehicle 100. Audio sensors 321 may include, but are not limited to,microphones and other types of acoustic-to-electric transducers orsensors. Optionally, the interior audio sensors 321 may be configured toreceive and convert sound waves into an equivalent analog or digitalsignal. The interior audio sensors 321 may serve to determine one ormore locations associated with various sounds in the vehicle 100. Thelocation of the sounds may be determined based on a comparison of volumelevels, intensity, and the like, between sounds detected by two or moreinterior audio sensors 321. For instance, a first audio sensors 321 maybe located in a first area of the vehicle 100 and a second audio sensors321 may be located in a second area of the vehicle 100. If a sound isdetected at a first volume level by the first audio sensors 321 A and asecond, higher, volume level by the second audio sensors 321 in thesecond area of the vehicle 100, the sound may be determined to be closerto the second area of the vehicle 100. As can be appreciated, the numberof sound receivers used in a vehicle 100 may be increased (e.g., morethan two, etc.) to increase measurement accuracy surrounding sounddetection and location, or source, of the sound (e.g., viatriangulation, etc.).

The safety sensors may comprise sensors configured to collect datarelating to the safety of a user and/or one or more components of avehicle 100. Examples of safety sensors may include one or more of, butare not limited to: force sensors 325, mechanical motion sensors 327,orientation sensors 329, restraint sensors 331, and more.

The force sensors 325 may include one or more sensors inside the vehicle100 configured to detect a force observed in the vehicle 100. Oneexample of a force sensor 325 may include a force transducer thatconverts measured forces (e.g., force, weight, pressure, etc.) intooutput signals. Mechanical motion sensors 327 may correspond toencoders, accelerometers, damped masses, and the like. Optionally, themechanical motion sensors 327 may be adapted to measure the force ofgravity (i.e., G-force) as observed inside the vehicle 100. Measuringthe G-force observed inside a vehicle 100 can provide valuableinformation related to a vehicle's acceleration, deceleration,collisions, and/or forces that may have been suffered by one or moreusers in the vehicle 100. Orientation sensors 329 can includeaccelerometers, gyroscopes, magnetic sensors, and the like that areconfigured to detect an orientation associated with the vehicle 100.

The restraint sensors 331 may correspond to sensors associated with oneor more restraint devices and/or systems in a vehicle 100. Seatbelts andairbags are examples of restraint devices and/or systems. As can beappreciated, the restraint devices and/or systems may be associated withone or more sensors that are configured to detect a state of thedevice/system. The state may include extension, engagement, retraction,disengagement, deployment, and/or other electrical or mechanicalconditions associated with the device/system.

The associated device sensors 323 can include any sensors that areassociated with a device in the vehicle 100. As previously stated,typical devices may include smart phones, tablets, laptops, mobilecomputers, and the like. It is anticipated that the various sensorsassociated with these devices can be employed by the longitudinalplanning system 348. For example, a typical smart phone can include, animage sensor, an IR sensor, audio sensor, gyroscope, accelerometer,wireless network sensor, fingerprint reader, and more. It is an aspectof the present disclosure that one or more of these associated devicesensors 323 may be used by one or more subsystems of the vehicle 100.

FIG. 3C illustrates a GPS/Navigation subsystem(s) 302. The navigationsubsystem(s) 302 can be any present or future-built navigation systemthat may use location data, for example, from the Global PositioningSystem (GPS), to provide navigation information or control the vehicle100. The navigation subsystem(s) 302 can include several components,such as, one or more of, but not limited to: a GPS Antenna/receiver 331,a location module 333, a maps database 335, etc. Generally, the severalcomponents or modules 331-335 may be hardware, software, firmware,computer readable media, or combinations thereof.

A GPS Antenna/receiver 331 can be any antenna, GPS puck, and/or receivercapable of receiving signals from a GPS satellite or other navigationsystem. The signals may be demodulated, converted, interpreted, etc. bythe GPS Antenna/receiver 331 and provided to the location module 333.Thus, the GPS Antenna/receiver 331 may convert the time signals from theGPS system and provide a location (e.g., coordinates on a map) to thelocation module 333. Alternatively, the location module 333 caninterpret the time signals into coordinates or other locationinformation.

The location module 333 can be the controller of the satellitenavigation system designed for use in the vehicle 100. The locationmodule 333 can acquire position data, as from the GPS Antenna/receiver331, to locate the user or vehicle 100 on a road in the unit's mapdatabase 335. Using the road database 335, the location module 333 cangive directions to other locations along roads also in the database 335.When a GPS signal is not available, the location module 333 may applydead reckoning to estimate distance data from sensors 304 including oneor more of, but not limited to, a speed sensor attached to the drivetrain of the vehicle 100, a gyroscope, an accelerometer, etc.Additionally, or alternatively, the location module 333 may use knownlocations of Wi-Fi hotspots, cell tower data, etc. to determine theposition of the vehicle 100, such as by using time difference of arrival(TDOA) and/or frequency difference of arrival (FDOA) techniques.

The maps database 335 can include any hardware and/or software to storeinformation about maps, geographical information system (GIS)information, location information, etc. The maps database 335 caninclude any data definition or other structure to store the information.Generally, the maps database 335 can include a road database that mayinclude one or more vector maps of areas of interest. Street names,street numbers, house numbers, and other information can be encoded asgeographic coordinates so that the user can find some desireddestination by street address. Points of interest (waypoints) can alsobe stored with their geographic coordinates. For example, a point ofinterest may include speed cameras, fuel stations, public parking, and“parked here” (or “you parked here”) information. The maps database 335may also include road or street characteristics, for example, speedlimits, location of stop lights/stop signs, lane divisions, schoollocations, etc. The map database contents can be produced or updated bya server connected through a wireless system in communication with theInternet, even as the vehicle 100 is driven along existing streets,yielding an up-to-date map.

The longitudinal planning system 348, when operating in L4 or L5 andbased on sensor information from the external and interior vehiclesensors, can control the driving behavior of the vehicle in response tothe current vehicle location, sensed object information, sensed vehicleoccupant information, vehicle-related information, exteriorenvironmental information, and navigation information from the mapsdatabase 335.

The sensed object information refers to sensed information regardingobjects external to the vehicle. Examples include animate objects suchas animals and attributes thereof (e.g., animal type, current spatiallocation, current activity, etc.), and pedestrians and attributesthereof (e.g., identity, age, sex, current spatial location, currentactivity, etc.), and the like and inanimate objects and attributesthereof such as other vehicles (e.g., current vehicle state or activity(parked or in motion or level of automation currently employed),occupant or operator identity, vehicle type (truck, car, etc.), vehiclespatial location, etc.), curbs (topography and spatial location),potholes (size and spatial location), lane division markers (type orcolor and spatial locations), signage (type or color and spatiallocations such as speed limit signs, yield signs, stop signs, and otherrestrictive or warning signs), traffic signals (e.g., red, yellow, blue,green, etc.), buildings (spatial locations), walls (height and spatiallocations), barricades (height and spatial location), and the like.

The sensed occupant information refers to sensed information regardingoccupants internal to the vehicle. Examples include the number andidentities of occupants and attributes thereof (e.g., seating position,age, sex, gaze direction, biometric information, authenticationinformation, preferences, historic behavior patterns (such as current orhistorical user driving behavior, historical user route, destination,and waypoint preferences), nationality, ethnicity and race, languagepreferences (e.g., Spanish, English, Chinese, etc.), current occupantrole (e.g., operator or passenger), occupant priority ranking (e.g.,vehicle owner is given a higher ranking than a child occupant),electronic calendar information (e.g., Outlook™), and medicalinformation and history, etc.

The vehicle-related information refers to sensed information regardingthe selected vehicle. Examples include vehicle manufacturer, type,model, year of manufacture, current geographic location, current vehiclestate or activity (parked or in motion or level of automation currentlyemployed), vehicle specifications and capabilities, currently sensedoperational parameters for the vehicle, and other information.

The exterior environmental information refers to sensed informationregarding the external environment of the selected vehicle. Examplesinclude road type (pavement, gravel, brick, etc.), road condition (e.g.,wet, dry, icy, snowy, etc.), weather condition (e.g., outsidetemperature, pressure, humidity, wind speed and direction, etc.),ambient light conditions (e.g., time-of-day), degree of development ofvehicle surroundings (e.g., urban or rural), and the like.

In a typical implementation, the automated longitudinal planning system348, based on feedback from certain sensors, specifically the LIDAR andradar sensors positioned around the circumference of the vehicle,constructs a three-dimensional map in spatial proximity to the vehiclethat enables the automated longitudinal planning system 348 to identifyand spatially locate animate and inanimate objects. Other sensors, suchas inertial measurement units, gyroscopes, wheel encoders, sonarsensors, motion sensors to perform odometry calculations with respect tonearby moving exterior objects, and exterior facing cameras (e.g., toperform computer vision processing) can provide further contextualinformation for generation of a more accurate three-dimensional map. Thenavigation information is combined with the three-dimensional map toprovide short, intermediate and long-range course tracking and routeselection. The longitudinal planning system 348 processes real-worldinformation as well as GPS data, and driving speed to determineaccurately the precise position of each vehicle, down to a fewcentimeters all while making corrections for nearby animate andinanimate objects.

The longitudinal planning system 348 can process in substantial realtime the aggregate mapping information and models (or predicts) behaviorof occupants of the current vehicle and other nearby animate orinanimate objects and, based on the aggregate mapping information andmodeled behavior, issues appropriate commands regarding vehicleoperation. While some commands are hard-coded into the vehicle, such asstopping at red lights and stop signs, other responses are learned andrecorded by profile updates based on previous driving experiences.Examples of learned behavior include a slow-moving or stopped vehicle oremergency vehicle in a right lane suggests a higher probability that thecar following it will attempt to pass, a pot hole, rock, or otherforeign object in the roadway equates to a higher probability that adriver will swerve to avoid it, and traffic congestion in one lane meansthat other drivers moving in the same direction will have a higherprobability of passing in an adjacent lane or by driving on theshoulder.

FIG. 4 shows one embodiment of the instrument panel 400 of the vehicle100. The instrument panel 400 of vehicle 100 comprises a steering wheel410, a vehicle operational display 420 (e.g., configured to presentand/or display driving data such as speed, measured air resistance,vehicle information, entertainment information, etc.), one or moreauxiliary displays 424 (e.g., configured to present and/or displayinformation segregated from the operational display 420, entertainmentapplications, movies, music, etc.), a heads-up display 434 (e.g.,configured to display any information previously described including,but in no way limited to, guidance information such as route todestination, or obstacle warning information to warn of a potentialcollision, or some or all primary vehicle operational data such asspeed, resistance, etc.), a power management display 428 (e.g.,configured to display data corresponding to electric power levels ofvehicle 100, reserve power, charging status, etc.), and an input device432 (e.g., a controller, touchscreen, or other interface deviceconfigured to interface with one or more displays in the instrumentpanel or components of the vehicle 100. The input device 432 may beconfigured as a joystick, mouse, touchpad, tablet, 3D gesture capturedevice, etc.). In some embodiments, the input device 432 may be used tomanually maneuver a portion of the vehicle 100 into a charging position(e.g., moving a charging plate to a desired separation distance, etc.).

While one or more of displays of instrument panel 400 may betouch-screen displays, it should be appreciated that the vehicleoperational display may be a display incapable of receiving touch input.For instance, the operational display 420 that spans across an interiorspace centerline 404 and across both a first zone 408A and a second zone408B may be isolated from receiving input from touch, especially from apassenger. In some cases, a display that provides vehicle operation orcritical systems information and interface may be restricted fromreceiving touch input and/or be configured as a non-touch display. Thistype of configuration can prevent dangerous mistakes in providing touchinput where such input may cause an accident or unwanted control.

In some embodiments, one or more displays of the instrument panel 400may be mobile devices and/or applications residing on a mobile devicesuch as a smart phone. Additionally, or alternatively, any of theinformation described herein may be presented to one or more portions420A-N of the operational display 420 or other display 424, 428, 434. Inone embodiment, one or more displays of the instrument panel 400 may bephysically separated or detached from the instrument panel 400. In somecases, a detachable display may remain tethered to the instrument panel.

The portions 420A-N of the operational display 420 may be dynamicallyreconfigured and/or resized to suit any display of information asdescribed. Additionally, or alternatively, the number of portions 420A-Nused to visually present information via the operational display 420 maybe dynamically increased or decreased as required and are not limited tothe configurations shown.

FIG. 5 illustrates a hardware diagram of communications componentry thatcan be optionally associated with the vehicle 100 in accordance withembodiments of the present disclosure.

The communications componentry can include one or more wired or wirelessdevices such as a transceiver(s) and/or modem that allows communicationsnot only between the various systems disclosed herein but also withother devices, such as devices on a network, and/or on a distributednetwork such as the Internet and/or in the cloud and/or with othervehicle(s).

The communications subsystem 350 can also include inter- andintra-vehicle communications capabilities such as hotspot and/or accesspoint connectivity for any one or more of the vehicle occupants and/orvehicle-to-vehicle communications.

Additionally, and while not specifically illustrated, the communicationssubsystem 350 can include one or more communications links (that can bewired or wireless) and/or communications busses (managed by the busmanager 574), including one or more of CANbus, OBD-II, ARCINC 429,Byteflight, CAN (Controller Area Network), D2B (Domestic Digital Bus),FlexRay, DC-BUS, IDB-1394, IEBus, I2C, ISO 9141-1/-2, J1708, J1587,J1850, J1939, ISO 11783, Keyword Protocol 2000, LIN (Local InterconnectNetwork), MOST (Media Oriented Systems Transport), Multifunction VehicleBus, SMARTwireX, SPI, VAN (Vehicle Area Network), and the like or ingeneral any communications protocol and/or standard(s).

The various protocols and communications can be communicated one or moreof wirelessly and/or over transmission media such as single wire,twisted pair, fiber optic, IEEE 1394, MIL-STD-1553, MIL-STD-1773,power-line communication, or the like. (All of the above standards andprotocols are incorporated herein by reference in their entirety).

As discussed, the communications subsystem 350 enables communicationsbetween any of the inter-vehicle systems and subsystems as well ascommunications with non-collocated resources, such as those reachableover a network such as the Internet.

The communications subsystem 350, in addition to well-known componentry(which has been omitted for clarity), includes interconnected elementsincluding one or more of: one or more antennas 504, aninterleaver/deinterleaver 508, an analog front end (AFE) 512,memory/storage/cache 516, controller/microprocessor 520, MAC circuitry522, modulator/demodulator 524, encoder/decoder 528, a plurality ofconnectivity managers 534, 558, 562, 566, GPU 540, accelerator 544, amultiplexer/demultiplexer 552, transmitter 570, receiver 572 andadditional wireless radio components such as a Wi-Fi PHY/Bluetooth®module 580, a Wi-Fi/BT MAC module 584, additional transmitter(s) 588 andadditional receiver(s) 592. The various elements in the device 350 areconnected by one or more links/busses 5 (not shown, again for sake ofclarity).

The device 350 can have one more antennas 504, for use in wirelesscommunications such as multi-input multi-output (MIMO) communications,multi-user multi-input multi-output (MU-MIMO) communications Bluetooth®,LTE, 4G, 5G, Near-Field Communication (NFC), etc., and in general forany type of wireless communications. The antenna(s) 504 can include, butare not limited to one or more of directional antennas, omnidirectionalantennas, monopoles, patch antennas, loop antennas, microstrip antennas,dipoles, and any other antenna(s) suitable for communicationtransmission/reception. In an exemplary embodiment,transmission/reception using MIMO may require particular antennaspacing. In another exemplary embodiment, MIMO transmission/receptioncan enable spatial diversity allowing for different channelcharacteristics at each of the antennas. In yet another embodiment, MIMOtransmission/reception can be used to distribute resources to multipleusers for example within the vehicle 100 and/or in another vehicle.

Antenna(s) 504 generally interact with the Analog Front End (AFE) 512,which is needed to enable the correct processing of the receivedmodulated signal and signal conditioning for a transmitted signal. TheAFE 512 can be functionally located between the antenna and a digitalbaseband system in order to convert the analog signal into a digitalsignal for processing and vice-versa.

The subsystem 350 can also include a controller/microprocessor 520 and amemory/storage/cache 516. The subsystem 350 can interact with thememory/storage/cache 516 which may store information and operationsnecessary for configuring and transmitting or receiving the informationdescribed herein. The memory/storage/cache 516 may also be used inconnection with the execution of application programming or instructionsby the controller/microprocessor 520, and for temporary or long-termstorage of program instructions and/or data. As examples, thememory/storage/cache 520 may comprise a computer-readable device, RAM,ROM, DRAM, SDRAM, and/or other storage device(s) and media.

The controller/microprocessor 520 may comprise a general-purposeprogrammable processor or controller for executing applicationprogramming or instructions related to the subsystem 350. Furthermore,the controller/microprocessor 520 can perform operations for configuringand transmitting/receiving information as described herein. Thecontroller/microprocessor 520 may include multiple processor cores,and/or implement multiple virtual processors. Optionally, thecontroller/microprocessor 520 may include multiple physical processors.By way of example, the controller/microprocessor 520 may comprise aspecially configured Application Specific Integrated Circuit (ASIC) orother integrated circuit, a digital signal processor(s), a controller, ahardwired electronic or logic circuit, a programmable logic device orgate array, a special purpose computer, or the like.

The subsystem 350 can further include a transmitter(s) 570, 588 andreceiver(s) 572, 592 which can transmit and receive signals,respectively, to and from other devices, subsystems and/or otherdestinations using the one or more antennas 504 and/or links/busses.Included in the subsystem 350 circuitry is the medium access control orMAC Circuitry 522. MAC circuitry 522 provides for controlling access tothe wireless medium. In an exemplary embodiment, the MAC circuitry 522may be arranged to contend for the wireless medium and configure framesor packets for communicating over the wired/wireless medium.

The subsystem 350 can also optionally contain a security module (notshown). This security module can contain information regarding but notlimited to, security parameters required to connect the device to one ormore other devices or other available network(s), and can include WEP orWPA/WPA-2 (optionally+AES and/or TKIP) security access keys, networkkeys, etc. The WEP security access key is a security password used byWi-Fi networks. Knowledge of this code can enable a wireless device toexchange information with an access point and/or another device. Theinformation exchange can occur through encoded messages with the WEPaccess code often being chosen by the network administrator. WPA is anadded security standard that is also used in conjunction with networkconnectivity with stronger encryption than WEP.

In some embodiments, the communications subsystem 350 also includes aGPU 540, an accelerator 544, a Wi-Fi/BT/BLE (Bluetooth® Low-Energy) PHYmodule 580 and a Wi-Fi/BT/BLE MAC module 584 and optional wirelesstransmitter 588 and optional wireless receiver 592. In some embodiments,the GPU 540 may be a graphics processing unit, or visual processingunit, comprising at least one circuit and/or chip that manipulates andchanges memory to accelerate the creation of images in a frame bufferfor output to at least one display device. The GPU 540 may include oneor more of a display device connection port, printed circuit board(PCB), a GPU chip, a metal-oxide-semiconductor field-effect transistor(MOSFET), memory (e.g., single data rate random-access memory (SDRAM),double data rate random-access memory (DDR) RAM, etc., and/orcombinations thereof), a secondary processing chip (e.g., handling videoout capabilities, processing, and/or other functions in addition to theGPU chip, etc.), a capacitor, heatsink, temperature control or coolingfan, motherboard connection, shielding, and the like.

The various connectivity managers 534, 558, 562, 566 manage and/orcoordinate communications between the subsystem 350 and one or more ofthe systems disclosed herein and one or more other devices/systems. Theconnectivity managers 534, 558, 562, 566 include a charging connectivitymanager 534, a vehicle database connectivity manager 558, a remoteoperating system connectivity manager 562, and a sensor connectivitymanager 566.

The charging connectivity manager 534 can coordinate not only thephysical connectivity between the vehicle 100 and a chargingdevice/vehicle but can also communicate with one or more of a powermanagement controller, one or more third parties, and optionally abilling system(s). As an example, the vehicle 100 can establishcommunications with the charging device/vehicle to one or more ofcoordinate interconnectivity between the two (e.g., by spatiallyaligning the charging receptacle on the vehicle with the charger on thecharging vehicle) and optionally share navigation information. Oncecharging is complete, the amount of charge provided can be tracked andoptionally forwarded to, for example, a third party for billing. Inaddition to being able to manage connectivity for the exchange of power,the charging connectivity manager 534 can also communicate information,such as billing information to the charging vehicle and/or a thirdparty. This billing information could be, for example, the owner of thevehicle, the driver/occupant(s) of the vehicle, company information, orin general any information usable to charge the appropriate entity forthe power received.

The vehicle database connectivity manager 558 allows the subsystem toreceive and/or share information stored in the vehicle database. Thisinformation can be shared with other vehicle components/subsystemsand/or other entities, such as third parties and/or charging systems.The information can also be shared with one or more vehicle occupantdevices, such as an app (application) on a mobile device the driver usesto track information about the vehicle 100 and/or a dealer orservice/maintenance provider. In general, any information stored in thevehicle database can optionally be shared with any one or more otherdevices optionally subject to any privacy or confidentiallyrestrictions.

The remote operating system connectivity manager 562 facilitatescommunications between the vehicle 100 and any one or more autonomousvehicle systems. These communications can include one or more ofnavigation information, vehicle information, other vehicle information,weather information, occupant information, or in general any informationrelated to the remote operation of the vehicle 100.

The sensor connectivity manager 566 facilitates communications betweenany one or more of the vehicle sensors (e.g., the driving vehiclesensors and systems 304, etc.) and any one or more of the other vehiclesystems. The sensor connectivity manager 566 can also facilitatecommunications between any one or more of the sensors and/or vehiclesystems and any other destination, such as a service company, app, or ingeneral to any destination where sensor data is needed.

In accordance with one exemplary embodiment, any of the communicationsdiscussed herein can be communicated via the conductor(s) used forcharging. One exemplary protocol usable for these communications isPower-line communication (PLC). PLC is a communication protocol thatuses electrical wiring to simultaneously carry both data, andAlternating Current (AC) electric power transmission or electric powerdistribution. It is also known as power-line carrier, power-line digitalsubscriber line (PDSL), mains communication, power-linetelecommunications, or power-line networking (PLN). For DC environmentsin vehicles PLC can be used in conjunction with CAN-bus, LIN-bus overpower line (DC-LIN) and DC-BUS.

The communications subsystem can also optionally manage one or moreidentifiers, such as an IP (Internet Protocol) address(es), associatedwith the vehicle and one or other system or subsystems or componentsand/or devices therein. These identifiers can be used in conjunctionwith any one or more of the connectivity managers as discussed herein.

FIG. 6 illustrates a block diagram of a computing environment 600 thatmay function as the servers, user computers, or other systems providedand described herein. The computing environment 600 includes one or moreuser computers, or computing devices, such as a vehicle computing device604, a communication device 608, and/or more 612. The computing devices604, 608, 612 may include general purpose personal computers (including,merely by way of example, personal computers, and/or laptop computersrunning various versions of Microsoft Corp.'s Windows® and/or AppleCorp.'s Macintosh® operating systems) and/or workstation computersrunning any of a variety of commercially-available UNIX® or UNIX-likeoperating systems. These computing devices 604, 608, 612 may also haveany of a variety of applications, including for example, database clientand/or server applications, and web browser applications. Alternatively,the computing devices 604, 608, 612 may be any other electronic device,such as a thin-client computer, Internet-enabled mobile telephone,and/or personal digital assistant, capable of communicating via anetwork 352 and/or displaying and navigating web pages or other types ofelectronic documents or information. Although the exemplary computingenvironment 600 is shown with two computing devices, any number of usercomputers or computing devices may be supported.

The computing environment 600 may also include one or more servers 614,616. In this example, server 614 is shown as a web server and server 616is shown as an application server. The web server 614, which may be usedto process requests for web pages or other electronic documents fromcomputing devices 604, 608, 612. The web server 614 can be running anoperating system including any of those discussed above, as well as anycommercially-available server operating systems. The web server 614 canalso run a variety of server applications, including SIP (SessionInitiation Protocol) servers, HTTP(s) servers, FTP servers, CGI servers,database servers, Java® servers, and the like. In some instances, theweb server 614 may publish operations available operations as one ormore web services.

The computing environment 600 may also include one or more file andor/application servers 616, which can, in addition to an operatingsystem, include one or more applications accessible by a client runningon one or more of the computing devices 604, 608, 612. The server(s) 616and/or 614 may be one or more general purpose computers capable ofexecuting programs or scripts in response to the computing devices 604,608, 612. As one example, the server 616, 614 may execute one or moreweb applications. The web application may be implemented as one or morescripts or programs written in any programming language, such as Java®,C, C#®, or C++, and/or any scripting language, such as Perl, Python, orTCL, as well as combinations of any programming/scripting languages. Theapplication server(s) 616 may also include database servers, includingwithout limitation those commercially available from Oracle®,Microsoft®, Sybase®, IBM® and the like, which can process requests fromdatabase clients running on a computing device 604, 608, 612.

The web pages created by the server 614 and/or 616 may be forwarded to acomputing device 604, 608, 612 via a web (file) server 614, 616.Similarly, the web server 614 may be able to receive web page requests,web services invocations, and/or input data from a computing device 604,608, 612 (e.g., a user computer, etc.) and can forward the web pagerequests and/or input data to the web (application) server 616. Infurther embodiments, the server 616 may function as a file server.Although for ease of description, FIG. 6 illustrates a separate webserver 614 and file/application server 616, those skilled in the artwill recognize that the functions described with respect to servers 614,616 may be performed by a single server and/or a plurality ofspecialized servers, depending on implementation-specific needs andparameters. The computer systems 604, 608, 612, web (file) server 614and/or web (application) server 616 may function as the system, devices,or components described in FIGS. 1-6 .

The computing environment 600 may also include a database 618. Thedatabase 618 may reside in a variety of locations. By way of example,database 618 may reside on a storage medium local to (and/or residentin) one or more of the computers 604, 608, 612, 614, 616. Alternatively,it may be remote from any or all of the computers 604, 608, 612, 614,616, and in communication (e.g., via the network 352) with one or moreof these. The database 618 may reside in a storage-area network (“SAN”)familiar to those skilled in the art. Similarly, any necessary files forperforming the functions attributed to the computers 604, 608, 612, 614,616 may be stored locally on the respective computer and/or remotely, asappropriate. The database 618 may be a relational database, such asOracle 20i®, that is adapted to store, update, and retrieve data inresponse to SQL-formatted commands.

FIG. 7 illustrates one embodiment of a computer system 700 upon whichthe servers, user computers, computing devices, or other systems orcomponents described above may be deployed or executed. The computersystem 700 is shown comprising hardware elements that may beelectrically coupled via a bus 704. The hardware elements may includeone or more central processing units (CPUs) 708; one or more inputdevices 712 (e.g., a mouse, a keyboard, etc.); and one or more outputdevices 716 (e.g., a display device, a printer, etc.). The computersystem 700 may also include one or more storage devices 720. By way ofexample, storage device(s) 720 may be disk drives, optical storagedevices, solid-state storage devices such as a random-access memory(“RAM”) and/or a read-only memory (“ROM”), which can be programmable,flash-updateable and/or the like.

The computer system 700 may additionally include a computer-readablestorage media reader 724; a communications system 728 (e.g., a modem, anetwork card (wireless or wired), an infra-red communication device,etc.); and working memory 736, which may include RAM and ROM devices asdescribed above. The computer system 700 may also include a processingacceleration unit 732, which can include a DSP, a special-purposeprocessor, and/or the like.

The computer-readable storage media reader 724 can further be connectedto a computer-readable storage medium, together (and, optionally, incombination with storage device(s) 720) comprehensively representingremote, local, fixed, and/or removable storage devices plus storagemedia for temporarily and/or more permanently containingcomputer-readable information. The communications system 728 may permitdata to be exchanged with a network and/or any other computer describedabove with respect to the computer environments described herein.Moreover, as disclosed herein, the term “storage medium” may representone or more devices for storing data, including read only memory (ROM),random-access memory (RAM), magnetic RAM, core memory, magnetic diskstorage mediums, optical storage mediums, flash memory devices and/orother machine-readable mediums for storing information.

The computer system 700 may also comprise software elements, shown asbeing currently located within a working memory 736, including anoperating system 740 and/or other code 744. It should be appreciatedthat alternate embodiments of a computer system 700 may have numerousvariations from that described above. For example, customized hardwaremight also be used and/or particular elements might be implemented inhardware, software (including portable software, such as applets), orboth. Further, connection to other computing devices such as networkinput/output devices may be employed.

Examples of the processors 340, 708 as described herein may include, butare not limited to, at least one of Qualcomm® Snapdragon® 800 and 801,Qualcomm® Snapdragon® 620 and 615 with 4G LTE Integration and 64-bitcomputing, Apple® A7 processor with 64-bit architecture, Apple® M7motion coprocessors, Samsung® Exynos® series, the Intel® Core™ family ofprocessors, the Intel® Xeon® family of processors, the Intel® Atom™family of processors, the Intel Itanium® family of processors, Intel®Core® i5-4670K and i7-4770K 22 nm Haswell, Intel® Core® i5-3570K 22 nmIvy Bridge, the AMD® FX™ family of processors, AMD® FX-4300, FX-6300,and FX-8350 32 nm Vishera, AMD® Kaveri processors, Texas Instruments®Jacinto C6000™ automotive infotainment processors, Texas Instruments®OMAP™ automotive-grade mobile processors, ARM® Cortex™-M processors,ARM® Cortex-A and ARM926EJ-S™ processors, other industry-equivalentprocessors, and may perform computational functions using any known orfuture-developed standard, instruction set, libraries, and/orarchitecture.

What is needed is a longitudinal planning system capable ofautomatically planning and executing a smooth and safe velocity changemaneuver. For example, an autonomous or semi-autonomous vehicle drivingin traffic may be capable of determining a velocity change maneuver isoptimal compared to a current state of the vehicle.

As used herein, ego vehicle refers to the vehicle containing thelongitudinal planning system being described while obstacle vehiclerefers to any other vehicle.

The range of reachable velocities of the vehicle may be limited by upperand lower limits on the acceleration of the vehicle. Note thatacceleration is not necessarily positive and thus the lower limit on theacceleration may be a negative acceleration and thus may be described asdeceleration. As such, the lower limit may be a negative rate ofacceleration, for example the vehicle may coast or slightly brake toreduce its rate of speed. The upper limit may be a positive rate ofacceleration, for example the vehicle may increase its speed.

The upper and lower acceleration limits may be vehicle-dependent. Thelimits may depend on factors such as driver and passenger comfort andsafety, road conditions, vehicle capabilities, etc.

As described herein, the velocity change maneuver should be optimalbased on a consideration of a number of factors. For example, thelongitudinal planning system should optimize obstacle avoidance.Obstacles to avoid may comprise other moving vehicles. The vehicle maystore and update a database of nearby vehicles and track data on suchnearby vehicles. For example, a vehicle-obstacle database may track dataassociated with other vehicles including an identification of a lane inwhich each obstacle vehicle is travelling, a speed of each obstaclevehicle, an acceleration of each obstacle vehicle, a probability of anupcoming lane change of each obstacle vehicle, etc. The longitudinalplanning system may also track non-moving obstacles such as streetsigns, construction work, fences, etc.

A velocity change maneuver may also be chosen based on a speed requiredfor the maneuver. Factors affecting an optimal speed may include, butare not limited to, other vehicles, speed limits, weather and/or drivingconditions, upcoming turns according to a current route of the vehicle,ideal acceleration and/or deceleration limits, or other factors.

Memory System

As illustrated in FIG. 7 , the longitudinal planning system may be incommunication with a memory element such as a storage device 720 storingone or more databases. In some embodiments, a database may track one ormore moving obstacle vehicles. For example, as illustrated in FIG. 8A,the ego vehicle 8000 may be driving in a third lane 8023 of a five-lanehighway. As illustrated by the x/y axis 8030, the vehicles may betravelling in a +y direction. As used herein, the term longitudinaldistance may refer to a distance travelled by a vehicle in a straightline parallel to the lane lines. In the example illustrated in FIG. 8A,longitudinal distance may refer to a distance parallel to the y-axis.

A number of obstacle vehicles 8001-8006 may be driving near the egovehicle 8000 in one or more of the five lanes 8021-8025.

A goal of the longitudinal planning system may be to represent allpossible lateral states and dynamics of the ego vehicle while conservingmemory and processor requirements. To this aim, the longitudinalplanning system may use local identifiers and measure qualities ofobstacle vehicles based relative to the ego vehicle. In someembodiments, a speed of an obstacle vehicle may be measured as relativeto the ego vehicle. For example, if the ego vehicle is travelling at 60mph and a first obstacle vehicle is travelling at 62 mph, the egovehicle may record the obstacle vehicle's speed as +2 mph.

The ego vehicle 8000 may sense information associated with one or moreof the obstacle vehicles 8001-8006. For example, the ego vehicle 8000may determine a current lane, a speed, and/or anacceleration/deceleration of an obstacle vehicle. In some embodiments,values such as current lane, speed, acceleration, etc. may be determinedin comparison to the ego vehicle 8000. For example, if a first obstaclevehicle 8001 is traveling at 1% faster than the ego vehicle 8000, theego vehicle 8000 may record the speed of the first obstacle vehicle 8001as 1.01. Similarly, if another obstacle vehicle 8004 is traveling 5%slower than the ego vehicle 8000, the ego vehicle 8000 may record thespeed of the other obstacle vehicle 8004 as 0.95.

Likewise, acceleration of obstacle vehicles 8001-8006 may be determinedin comparison to the ego vehicle 8000. For example, an acceleration of0.0 may represent a vehicle is accelerating at a same rate as the egovehicle 8000. In some embodiments, acceleration may be measured in anactual value as opposed to as relative to the ego vehicle 8000. In someembodiments, if a first obstacle vehicle 8001 is accelerating at thesame rate as the ego vehicle 8000, the ego vehicle 8000 may record theacceleration of the first obstacle vehicle 8001 as 0.0. Similarly, if asecond obstacle vehicle 8002 is accelerating at a 10% greater rate thanthe ego vehicle 8000, the ego vehicle 8000 may record the accelerationof the other obstacle vehicle 8002 as 0.1.

The ego vehicle 8000 may also track a current lane 8021-8025 for one ormore of the obstacle vehicles 8001-8006. In some embodiments, the egovehicle 8000 may only track statistics of vehicle in the ego lane 8023as well as the two neighboring lanes 8022, 8024. By limiting the trackedvehicles to those lanes, the memory and processor requirements foranalyzing proper lane changes may be reduced. Lane identifiers used bythe ego vehicle 8000 may be recorded by the ego vehicle 8000 based on arelative position of each lane to the ego vehicle 8000. For example, theego lane 8022 containing the ego vehicle 8000 may be labelled as Lane00. The lane 8022 to the immediate left of the ego vehicle 8000 may bereferred to as Lane 10. The lane 8024 to the immediate right of the egovehicle 8000 may be referred to as Lane 01. In some embodiments, the egovehicle may store data associated only with obstacle vehicles in lanes00, 01, and 10.

The ego vehicle 8000 may also determine a location of one or more of theobstacle vehicles 8001-8006. In some embodiments, the location maysimply be a determination of a distance between a nearest edge of theego vehicle 8000 and one of the obstacle vehicles 8001-8006. Forsimplicity, the system may assume that any obstacle vehicle 8001-8006occupies the total width of its lane 8021-8025. The distance may, insome embodiments, be simply a measurement along the y-axis. Asillustrated in FIG. 8A, the ego vehicle 8000 may determine a distance8011 between the front bumper of the ego vehicle 8000 and the rearbumper of the obstacle vehicle 8001 directly in front of the ego vehicle8000. As used herein, a position of an obstacle may comprise both of alane ID and a distance of the obstacle from the ego vehicle.

Distance of each obstacle may be measured in terms of a relativedistance from the nearest edge of the ego vehicle. For example, in thecase of a leading car ahead of the ego vehicle in the same lane, theposition of the leading car may be measured in a distance from the frontbumper of the ego car to the back bumper of the leading car. Similarly,for an obstacle in a lane adjacent to the ego lane driving ahead of theego vehicle, the position distance of the obstacle may be measuredsimply in terms of the longitudinal distance from the front bumper ofthe ego vehicle to the obstacle.

The ego vehicle 8000 may determine a distance 8012 between the egovehicle 8000 and an obstacle vehicle 8002 in a neighboring lane 8024. Ifthe obstacle vehicle 8002 is at least slightly leading the ego vehicle8000, the distance 8012 may be measured from the front bumper of the egovehicle 8000 and the rear bumper of the obstacle vehicle 8002. Thedistance may, in some embodiments, be simply a measurement along they-axis to simplify the calculations required.

Similarly, the ego vehicle 8000 may determine a distance 8014 betweenthe ego vehicle 8000 and an obstacle vehicle 8004 behind the ego vehicle8000 in a neighboring lane 8024. If the obstacle vehicle 8004 is atleast slightly behind the ego vehicle 8000, the distance 8014 may bemeasured from the rear bumper of the ego vehicle 8000 and the frontbumper of the obstacle vehicle 8004.

In some embodiments, if an obstacle vehicle 8003 in a neighboring lane8022 is neither completely ahead of or completely behind the ego vehicle8000, the ego vehicle 8000 may record the location of the obstaclevehicle 8003 as a negative number associated with the distance 8013between the front bumper of the ego vehicle 8000 and the back bumper ofthe neighboring obstacle vehicle 8003.

As illustrated in FIG. 8B, a table 8040 may be stored in memory and maybe actively updated during travel. The table 8040 may store data foreach of the currently-monitored obstacle vehicles. To continue theexample illustrated in FIG. 8A, the ego vehicle 8000 may monitorstatistics for any vehicle in the ego lane 8023, the lane 8022 to theleft of the ego lane 8023 and the lane 8024 to the right of the ego lane8023. As illustrated in FIGS. 8A and 8B, the table 8040 may comprisedata for each of obstacles 8001-8004. The table may store data for eachof the obstacles 8001-8004 such as an obstacle ID, a lane ID of a lanecontaining each obstacle, a relative speed, a relative rate ofacceleration, a relative distance, and an indicator as to whether thedistance is measured from the front or rear/back bumper of the egovehicle 8000.

A longitudinal planning system may be capable of determining a vehicle'scurrent state in terms of its speed, distance from a leader vehicle, andthe speed of the leader vehicle. A longitudinal planning system maydetermine a control scheme for maneuvering the vehicle given thevehicle's current status. For example, if the vehicle is followingbehind a leader or obstacle vehicle, the longitudinal planning systemmay determine accelerating or decelerating the vehicle at a particularrate may be preferred.

The longitudinal planning system may also be capable of automaticallycarrying out a maneuver given a specific control scheme. For example,the longitudinal planning system may determine a specific moment toinitiate the maneuver and at what rate to change the acceleration of thevehicle.

In some embodiments, a vehicle may have an optimal maximum and minimumrate of acceleration for a maneuver. The optimal maximum and minimumrates of acceleration may be determined based on a number of factors,such as safety, comfort, etc. Note that the optimal maximum accelerationmay not be the total possible maximum acceleration of the vehicle andthe optimal minimum acceleration may not be the total possible minimumacceleration (which would be hitting the brakes to slow the vehicle asquickly as possible). In some embodiments, the optimal minimumacceleration may equivalent to a gentle pressing of the brakes.

With regards to FIGS. 9A and 9B, note that the input is the relativeacceleration a_(ego)−a_(leader). The maximum relative acceleration canhave arbitrary sign, but the longitudinal planning system may defineassumptions such that the minimum relative acceleration is alwaysnegative to simplify collision avoidance. That is, the ego vehicle mustbe capable of decelerating faster than the leader vehicle so as becapable of avoiding collision.

The states (distance and relative speed) for the problem are definedrelative to the leader, and the leader's known acceleration (which maybe assumed to be 0) may be factored in accordingly by defining thecontrol input to this system as the relative accelerationa_(rel)=a_(ego)−a_(leader).

Because these input bounds change with the leader acceleration, thelongitudinal planning system may re-solve the system on each piecewiseconstant acceleration segment of the leader's speed profile. That is,the longitudinal planning system may solve for how to converge to theleader and maintain the desired distance indefinitely (where thesteady-state is held by matching the leader's acceleration) and applythis policy until the leader's acceleration changes. At this transitionto the next segment in the leader's speed profile, the longitudinalplanning system may identify the new relative acceleration bounds andagain solve for the bang-bang speed profile given the new initialconditions.

Every state is reachable (can be reached by the ego vehicle from theorigin in some amount of time) under bang-bang control if theacceleration limits allow both positive and negative accelerations.Typically, the longitudinal planning system may assume boundaries on theabsolute acceleration to be a_(min)≤a_(ego)≤a_(max) where a_(min)<0 anda_(max)>0. However, dealing with calculations of the ego vehiclesacceleration relative to a leader vehicle's acceleration may be moredifficult as there is no guarantee of the range of the relativeacceleration, due to the dependency on the leader's acceleration. Toguarantee the ego vehicle's ability to reduce its relative speed (forcollision avoidance), the longitudinal planning system may make thesomewhat artificial assumption that each car can always deceleratefaster than its most proximal leader. Thus, in some cases if theacceleration of the leader becomes less than a_(min), the longitudinalplanning system may adjust a_(min) to be equal to a_(leader)−a_(min rel)for some minimum negative relative acceleration. This adjustment mayresult in the final lower bound of a_(rel) being negative.

The speed profiles or control schemes to be executed by the longitudinalplanning system in order to follow a leading vehicle at an optimalfollowing distance may be plotted on a phase plot as illustrated in FIG.9A.

In FIG. 9A, a two-axis graph 900 is illustrated. A vertical axis 903 mayillustrate a relative velocity of the ego vehicle compared to a leadingvehicle. The vertical axis 903 may plot the ego vehicle velocity minusthe leading vehicle velocity. As such, the higher a point is on thevertical axis 903, the faster the ego vehicle is travelling compared tothe leading vehicle. Points along the horizontal axis 906 may representthe ego and leading vehicles travelling at a same speed.

Points below the horizontal axis 906 may represent the ego vehicletravelling slower than the leading vehicle.

The horizontal axis 906 may plot a distance between the ego and leadingvehicles. The further to the right a point is on the graph 900 thefurther the ego vehicle is from the leading vehicle. The further to theleft a vehicle is on the graph 900 the closer the ego vehicle is to theleading vehicle. Points along the vertical axis 903 may represent theego vehicle physically touching the leading vehicle if the ego andleading vehicles are in the same lane. The graph may also be used torepresent a situation with a leading vehicle in a lane other than theego lane, in which case points along the vertical axis 903 may representthe ego vehicle beginning to pass the leading vehicle.

An origin of the graph 900 shown by the intersection of the horizontalaxis 906 and the vertical axis 903 may represent the ego vehiclephysically touching the leading vehicle, if travelling in the same lane,while travelling at the same speed.

Points in the upper left quadrant, above the horizontal axis 906 and tothe left of the vertical axis 903 may represent the ego vehicletravelling at a faster speed compared to the leading vehicle without anydistance between the ego and leading vehicles. If the ego and leadingvehicles are in the same lane, points in the upper left quadrant mayrepresent a collision.

Points in the upper right quadrant, above the horizontal axis 906 and tothe right of the vertical axis 903 may represent the ego vehicletravelling at a faster speed compared to the leading vehicle with apositive distance between the ego and leading vehicles.

Points in the lower right quadrant, below the horizontal axis 906 and tothe right of the vertical axis 903 may represent the ego vehicletravelling at a lower speed compared to the leading vehicle with apositive distance between the ego and leading vehicles.

Points in the lower left quadrant, below the horizontal axis 906 and tothe left of the vertical axis 903 may represent the ego vehicletravelling at a lower speed compared to the leading vehicle without anydistance between the ego and leading vehicles. If the ego and leadingvehicles are in the same lane, points in the lower left quadrant mayrepresent a collision.

To execute a velocity change maneuver in a scenario in which there is aleading vehicle in either the ego lane or a target lane, thelongitudinal planning system of the ego vehicle may determine an optimalfollowing distance to follow behind the leading vehicle. An optimalfollowing distance should be on the right side of the vertical axis 903,representing a positive distance between the ego vehicle and the leadingvehicle.

In some circumstances, the longitudinal planning system may determine arate of speed equal to that of the leading vehicle is optimal to executea velocity change maneuver. In such circumstances, the optimal positionfor the ego vehicle will be on the horizontal axis 906 and to the rightof the vertical axis 903.

Arrows 909, 912, 915, 918, 921, 924, 927, 930, 933, and 936 representvector goal trajectories for an ego vehicle to reach an optimalfollowing distance represented by the point 939 indicated in FIG. 9A.

For example, a longitudinal planning system of an ego vehicle at thetail end of the arrow 915 may determine the ego vehicle is too close tothe leading vehicle and travelling too fast in comparison to the leadingvehicle. In response to such a decision, the longitudinal planningsystem may determine a proper action is to decelerate at a maximumdeceleration, effectively hitting the brakes. As illustrated by thedirection of the arrow 915, the relative speed should greatly decreasewhile the distance between the vehicles should remain the same.

A vehicle at the tail end of the arrow 918 is travelling slower than theleading vehicle but is still too close to the leading vehicle. In such ascenario, the longitudinal planning system may determine a moderatedeceleration is appropriate as indicated by the arrow 918.

A vehicle at the tail end of the arrow 921 is travelling slower than theleading vehicle but is closer to the leading vehicle than the optimalfollowing distance 939. In such a scenario, the longitudinal planningsystem may determine maintaining a same velocity differential comparedto the leading vehicle is appropriate as indicated by the arrow 921. Forexample, if both the ego and leading vehicles are driving at constantspeeds, and the ego vehicle is driving slightly slower than the leadingvehicle, the ego vehicle may continue driving at the slower but constantspeed until reaching a point near the tail end of the arrow 924.

A vehicle at the tail end of the arrow 924 is travelling slower than theleading vehicle but is now further from the leading vehicle than theoptimal distance 939. In such a scenario, the longitudinal planningsystem may determine a moderate acceleration is appropriate as indicatedby the arrow 924.

A vehicle at the tail end of the arrow 927 is travelling slightly slowerthan the leading vehicle and is still too far from the leading vehicle.In such a scenario, the longitudinal planning system may apply amoderate acceleration to follow the trajectory as indicated by the arrow927.

A vehicle at the tail end of the arrow 930 is travelling slightly fasterthan the leading vehicle but is still too far from the leading vehicle.In such a scenario, the longitudinal planning system may alter theacceleration of the vehicle to follow the trajectory indicated by thearrow 930.

A vehicle at the tail end of the arrow 933 is travelling faster than theleading vehicle but is still too far from the leading vehicle. In such ascenario, the longitudinal planning system may alter the acceleration ofthe vehicle to follow the trajectory indicated by the arrow 930.

A vehicle at the tail end of the arrow 936 is travelling faster than theleading vehicle but is still too far from the leading vehicle. In such ascenario, the longitudinal planning system may alter the acceleration ofthe vehicle to follow the trajectory indicated by the arrow 936.

A vehicle at the tail end of the arrow 909 is travelling much fasterthan the leading vehicle but is relatively close to the optimalfollowing distance 939. In such a scenario, the longitudinal planningsystem may alter the acceleration of the vehicle to follow thetrajectory indicated by the arrow 909.

A vehicle at the tail end of the arrow 912 is travelling much fasterthan the leading vehicle but is relatively close to the optimalfollowing distance 939. In such a scenario, the longitudinal planningsystem may alter the acceleration of the vehicle to follow thetrajectory indicated by the arrow 912.

A more detailed trajectory phase plane chart 950 is illustrated in FIG.9B. As can be appreciated, the acceleration plans executed by thelongitudinal planning system may be gradually executed as illustrated bythe curved vector arrows. In addition to determining an optimalfollowing distance 939, the longitudinal planning system may alsodetermine a critical following distance 962. The critical distance 962may be selected from distances which are less than optimal butrelatively safe. Vehicles at distances between the optimal followingdistance 939 and the critical distance 962 may be capable of returningto the optimal distance using relatively moderate acceleration profiles.

For example, the longitudinal planning system may adjust theacceleration of the vehicle between a minimum and a maximumacceleration. During normal operation of the vehicle, the longitudinalplanning system may set the minimum acceleration to a higher amount thanis physically possible. The minimum acceleration may be determined basedon comfort for the driver and passengers, road quality, weatherconditions, safety, etc.

Similarly, the maximum acceleration may be set to a lower amount than isphysically possible. The maximum may also be determined based on comfortfor the driver and passengers, road quality, weather conditions, safety,etc.

In most situations, it may be possible to stay within the normal maximumand minimum boundaries for the acceleration.

For example, as illustrated in FIG. 9B, a vehicle at positions reflectedby the tail end of arrows may be capable of approaching the optimalfollowing distance by applying the minimum acceleration. Similarly, avehicle at positions reflected by the tail end of arrows may be capableof approaching the optimal following distance by applying the maximumacceleration. However, a vehicle at positions reflected by the tail endof arrows may not be capable of approaching the optimal followingdistance by staying within the normal maximum and minimum accelerationboundaries. Such a vehicle may be required to apply a total maximum ortotal minimum acceleration to avoid a collision with a leading vehicle.

As can be appreciated, in most scenarios, an ego vehicle may approach anoptimal following distance using a relatively simple control scheme. Bychoosing a maximum and minimum acceleration for the vehicle to be usedin normal operating conditions, the processor and memory requirements todetermine a proper speed profile to arrive at a particular followingdistance may be reduced.

As described above, every state is reachable (can be reached by the egovehicle from the origin in some amount of time) under bang-bang controlif the acceleration limits allow both positive and negativeaccelerations. Typically, the longitudinal planning system may assumeboundaries on the absolute acceleration to be a_(min)≤a_(go)≤a_(max)where a_(min)<0 and a_(max)>0. However, dealing with calculations of theego vehicles acceleration relative to a leader vehicle's accelerationmay be more difficult as there is no guarantee of the range of therelative acceleration, a_(rel) due to the dependency on the leader'sacceleration. To guarantee the ego vehicle's ability to reduce itsrelative speed (for collision avoidance), the longitudinal planningsystem may make the somewhat artificial assumption that each car canalways decelerate faster than its most proximal leader. Thus, in somecases if the acceleration of the leader becomes less than a_(min), thelongitudinal planning system may adjust a_(min) to be equal toa_(leader)−a_(min rel) for some minimum negative relative acceleration.This adjustment may result in the final lower bound of a_(rel) beingnegative. Meanwhile, there may not be a practical need to guarantee theego vehicle's ability to accelerate faster than the leader, it ispossible to have the maximum are′ be zero or even less than zero (whenthe leader is accelerating quickly). As accelerating slower than aleading vehicle may not lead to a collision or other dangeroussituation. This allows for the basic nature of the bang-bang phaseportrait to be modified and the equilibrium conditions may be changed.If the relative maximum acceleration (the acceleration of the leaderminus the maximum acceleration of the ego vehicle) is zero, everythingbelow the horizontal axis is an equilibrium in that the relative speedis constant (but the distance between the vehicles is always increasingas the ego vehicle is travelling slower than the leading vehicle). Ifthe maximum relative acceleration is less than zero, i.e., the leadingvehicle is accelerating at a greater rate than the ego vehicle iscapable of, everything below the horizontal axis is a steady-state inthat the ego vehicle may hold its maximum acceleration indefinitely, butthe speed difference is always decreasing and the distance is alwaysincreasing. Both of these cases require specific modifications to thealgebraic solver of the bang-bang system (the former is a linear systemthat is singular if represented quadratically, the latter is an“inverted” version whose relevant root is the opposite of the standardsystem).

In some situations, the leading vehicle may be accelerating at the samerate as the maximum acceleration of the ego vehicle. This maximumacceleration may be the ego vehicle's total maximum acceleration or maybe the maximum acceleration for normal operation of the vehicle whichmay be less than the ego vehicle's total maximum acceleration.

As illustrated in FIG. 9C, a vehicle may use a phase portrait to followa motion plan. The phase portrait may plot a series of possible motionplans. Motion plans may plot a trajectory for the ego vehicle to followgiven a distance between the ego vehicle and an ideal following distancefrom the vehicle. The distance from the ego vehicle to the idealfollowing distance may be plotted on an x-axis as illustrated in FIG.9C. The difference between the ego vehicle and the leader vehicle may beplotted on a y-axis. The vehicle control system may determine the egovehicle's position on the phase portrait and may then use the motionplan for that position to determine a trajectory for the ego vehicle tofollow.

In the phase portrait of FIG. 9C, the ideal following distance of theego vehicle is 8.6 meters behind the leader vehicle. This value shouldbe considered as given for example purposes only.

As illustrated in FIG. 9D, an ego vehicle with an ideal followingdistance of 40 m following a leader vehicle traveling at 25 m/s may usea similar phase portrait to follow a motion plan as discussed above withrelation to FIG. 9C. Note that in FIG. 9D, the y-axis plots the actualspeed of the ego vehicle as opposed to the relative speed of the egovehicle.

Similarly, as illustrated in FIG. 9E, a vehicle may use a phase portraitof LQR trajectories. LQR trajectories may be used by an LQR controllerto control the trajectory of the vehicle.

And as illustrated in FIG. 9F, an ego vehicle with an ideal followingdistance of 40 m following a leader vehicle traveling at 25 m/s may usea similar phase portrait of LQR trajectories to control the trajectoryof the vehicle. Note that in FIG. 9F, the y-axis plots the actual speedof the ego vehicle as opposed to the relative speed of the ego vehicle.

As illustrated in FIG. 9G, a phase portrait may comprise undershootand/or overshoot constraints for the ego vehicle to follow. For example,if the state of the vehicle is within the boundaries set by theundershoot and overshoot constraint lines of the phase portrait asillustrated in FIG. 9G, the vehicle may follow a typical control plan.If the vehicle crosses a constraint line, the vehicle may follow adifferent control plan. For example, if the vehicle state is above theundershoot constraint line of the phase portrait, the vehicle controlsystem may determine the vehicle is too close to the leader vehiclegiven the speed difference between the leader and ego vehicles.Similarly, if the vehicle state is below the overshoot constraint line,the vehicle control system may determine the ego vehicle is travelingtoo slow given the current scenario.

As illustrated in FIG. 9H, an ego vehicle with an ideal followingdistance of 40 m following a leader vehicle traveling at 25 m/s may usesimilar undershoot and/or overshoot constraints for the ego vehicle tofollow as discussed above with FIG. 9G. Note that in FIG. 9H, the y-axisplots the actual speed of the ego vehicle as opposed to the relativespeed of the ego vehicle.

A vehicle control system may utilize a braking leader criticalconstraint line as illustrated in FIG. 9I. Again, the x-axis of thephase portrait illustrated in FIG. 9I may plot a distance from the egovehicle and the leader vehicle. The y-axis of FIG. 9I may plot arelative speed of the ego vehicle as compared to the leader vehicle. Thevehicle control system may have a custom value for the proper followingdistance. In the example as illustrated in FIG. 9I, the distance is setat 8.6 m. A braking leader critical constraint line may set a barrierfrom which if the ego vehicle crosses above the braking leader criticalconstraint line the control system may determine the ego vehicle may notbe capable of stopping in time if the leader vehicle applies its brakesto a maximum amount. As such, the vehicle control system may follow adrastic control plan in such an event that the state of the ego vehicleis above the braking leader critical constraint line.

As illustrated in FIG. 9J, an ego vehicle with an ideal followingdistance of 40 m following a leader vehicle traveling at 25 m/s may usea similar braking leader critical constraint line as discussed above inFIG. 9I. Note that in FIG. 9J, the y-axis plots the actual speed of theego vehicle as opposed to the relative speed of the ego vehicle.

As illustrated in FIG. 9K, a vehicle control system for an ego vehiclemay utilize upper and lower LQR saturation boundaries. When a state ofan ego vehicle rises above an upper LQR saturation boundary or below alower LQR saturation boundary, the vehicle control system may implementLQR saturation in its control of the ego vehicle.

As illustrated in FIG. 9L, an ego vehicle with an ideal followingdistance of 40 m following a leader vehicle traveling at 25 m/s may usesimilar upper and lower LQR saturation boundaries as discussed abovewith FIG. 9K. Note that in FIG. 9L, the y-axis plots the actual speed ofthe ego vehicle as opposed to the relative speed of the ego vehicle.

Using the constraints and control plans as discussed herein, a vehiclemay follow an overall contour as illustrated in FIG. 10A. As illustratedin FIG. 10A, the relative speed and relative distance of the ego vehiclecompared with the leader vehicle may be plotted on y- and x-axesrespectively. In the scenario illustrated in FIG. 10A, the speed of theleader vehicle is considered to be zero as when the ego vehicle istraveling at the same speed the ego's speed is plotted on the x-axis andconsidered to be also at zero, or equilibrium. The ideal followingdistance for the ego vehicle is set to be 8.6 meters, but this should beconsidered as being for example purposes only. As illustrated in theplot of FIG. 10A, as the ego vehicle travels at a high relative speedand becomes closer and closer to the leader vehicle, the contour plot ofthe ego vehicle becomes more and more extreme. A vertical lineillustrates a maximum deceleration of the ego vehicle.

As illustrated in FIG. 10B, a contour plot of total control of the egovehicle looks drastically different when the y-axis plots the actualspeed of ego vehicle. In the contour plot of FIG. 10B, the leadervehicle is traveling at a constant speed of 25 m/s and the idealfollowing distance is set at 38.6 meters.

Speed Profile Generation

Utilizing systems described above and herein, a longitudinal planningsystem of an ego vehicle may be enabled to execute a longitudinalplanner for the ego vehicle. The longitudinal planner may effectively bean artificial intelligence system capable of generating a speed profilefor the vehicle. The speed profile may be in the form of instructionsexecutable by the longitudinal planning system. Using a speed profilegenerated using a longitudinal planner as described herein, thelongitudinal planning system may be capable of automatically managingthe speed of the vehicle in order to keep the vehicle at a properdistance from a leader vehicle. In the event that no leader vehicle isin front of the ego vehicle, the ego vehicle may follow a particular setof instructions such as following a speed limit, driving at a safe speedgiven current weather and road conditions, etc. The longitudinal plannermay be capable of ensuring avoidance of collision with a leading vehiclegiven certain assumptions; tracking a specified amount of headway timebetween the vehicle and the leader vehicle; obeying specified speedlimits; following feedforward rules (e.g., from a lane change proceduresystem) if safe; applying limited acceleration and deceleration; and/ormaintaining passenger comfort.

In some embodiments, a longitudinal planning system of an ego vehiclemay use as inputs a distance between the ego vehicle and the leadervehicle, a measured or estimated velocity of the leader vehicle, and themeasured or estimated velocity of the ego vehicle. Such inputs may bemeasured by systems of the ego vehicle. For example, the distancebetween the ego vehicle and the leader vehicle may be measured by aradar system or other system.

The longitudinal planning system may output a control plan for the egovehicle. For example, the control plan may be an acceleration to becarried out by of the ego vehicle. In some embodiments, the longitudinalplanning system may assume that the possible acceleration of the egovehicle is bounded but can change instantaneously (i.e. no jerkconstraints). The longitudinal planning system may also assume theleader vehicle has a bounded deceleration and will never drivebackwards.

In some embodiments, a goal of the longitudinal planner may be todetermine and designate an ideal or proper following distance (d*)behind a nearest leading vehicle. For example, the d* may be a distance(e.g. meters) such that if the ego vehicle is following at a distance ofd* behind the leader vehicle the ego vehicle can be assuredly capable ofcoming to a complete and safe stop even in the extreme circumstance thatthe leader vehicle immediately brakes to its maximum capability.

The proper following distance (d*) may correspond to a target followingtime, t_(follow). The t_(follow) may represent the amount of time (e.g.seconds) that it would take the ego vehicle to reach the baselinedistance behind the leader vehicle if the leader vehicle were toinstantaneously come to a stop in front of the ego vehicle and the egovehicle continued at its current speed none-the-less. In someembodiments, the proper following distance, d*, may be related to thetarget following time, t_(follow), by a formula such as:d*=d₀+v_(e)×t_(follow), where d₀ is a baseline distance (e.g. in meters)reflecting a safe distance between the ego vehicle and the leadervehicle when the vehicles are not moving. For example, if the leadervehicle stops the ego vehicle should stop behind it at a distance equalto d₀. The symbol v_(e) represents a current velocity of the egovehicle.

Assuming that the leader vehicle velocity, vi, remains constant (makingthe acceleration of the leader vehicle, a_(leader), equal to zero), theerror dynamics of the system may be represented as follows:

${{\frac{d}{dt}\begin{bmatrix}{d - d^{*}} \\{v_{e} - v_{l}}\end{bmatrix}} = {{\begin{bmatrix}0 & 1 \\0 & 0\end{bmatrix}\begin{bmatrix}{d - d^{*}} \\{v_{e} - v_{l}}\end{bmatrix}} + {\begin{bmatrix}{- t_{follow}} \\1\end{bmatrix}u}}},$where u represents the target acceleration of the ego vehicle. In someembodiments, u, may be an output of the longitudinal planner to alongitudinal planning system in order to control the vehicle at theproper distance behind the leader vehicle. The longitudinal planningsystem may use the target acceleration to control the vehicle and keepthe vehicle at a proper speed given a state of the leader vehicle.

Given the dynamics of the matrices

${\begin{bmatrix}0 & 1 \\0 & 0\end{bmatrix}\mspace{14mu}{{and}\mspace{14mu}\begin{bmatrix}{- t_{follow}} \\1\end{bmatrix}}},$in the above error dynamics formula, cost matrices Q and R whichrespectively quadratically penalize a distance error (d−d*) and a speeddifference (v_(e)−v_(l)) may be defined. Next, an optimal linearfeedback control, u=−K[d−d*; v_(e)−v_(l)], may be solved for in theabsence of any constraints. Near the origin, i.e. when the ego andleader vehicle are travelling at or near the same speed and when the egoand leader vehicle are near in proximity, this feedback control mayprovide a smooth and continuous convergence to an equilibrium point.

Linear-quadratic regulator (LQR) control systems are naturally optimal,can achieve good phase margin, and are easy to tune in terms of reducingovershoot or oscillations. However, two primary short-comings of a LQRbased longitudinal planning system are that such a system does notanticipate any acceleration from a leader vehicle (e.g. example usingcritical constraints) and the system does not account for inputsaturations (e.g. using critical and/or other constraints). Otherwise,when the error is small and the saturation constraints are inactive, anLQR control system can be an optimal solution for smoothly tracking theorigin. For this reason, what is needed is a dynamic longitudinalplanning system capable of overcoming such short-comings. As describedherein, a longitudinal planning system may both account for leaderacceleration and account for input saturations using constraints.

Collision Avoidance

In some embodiments, the longitudinal planning system may assume that atany point in time the leader vehicle may instantaneously and/orspontaneously initiate braking at a maximum deceleration to a completestop. The longitudinal planning system may also assume that the leadervehicle has some maximum deceleration (a_(crit)), where a_(crit) is lessthan zero (i.e., deceleration). The longitudinal planning system mayassume that while the leader vehicle is braking, the magnitude of thedeceleration of the leader vehicle will not exceed its a_(crit) (forexample negative 5 m/s²) i.e., will not decelerate at a greater ratethan the assumed maximum deceleration. The longitudinal planning systemmay also assume that it can brake up to its own a_(crit) limit. Acritical following distance (d_(crit)) may be determined as a pointwhich is the minimum following distance for which the ego vehicle of theego and leader vehicles may safely stop behind the leader vehicle giventhe current velocity and the maximum deceleration for the leader and egovehicles. The longitudinal planning system may constantly attempt toenforce acceleration/deceleration as may be determined by thelongitudinal planning system dynamically. For example, the longitudinalplanning system may seek to guarantee its current following distance (d)is greater than or equal to d_(crit) at all times.

By algebraically solving kinematics for a constant acceleration motionprofile, it can be shown that, given a velocity of an ego vehicle(v_(e)), a leader speed (v_(l)) that is less than v_(e), and a currentdistance margin (with respect to the critical value) of d−d_(crit), andif it is assumed that both vehicles apply some constant decelerationa_(required) given by

${a_{required} = \frac{v_{l}^{2} - v_{e}^{2}}{2( {d - d_{crit}} )}},$the leader vehicle will come to a stop first (at which point theacceleration of the leader vehicle (a_(leader)) will change froma_(required) to zero), and the ego car will then later come to a stop atexactly d=d_(crit).

Derivation Outline

If both the ego and leader vehicles apply the same deceleration,a_(required), for a first amount of time (Δt₁=v_(l)/−a_(required) (thetime it takes the leader vehicle to stop)), the relative velocity of theego and leader vehicles, v_(e)−v_(l) should not change. During thistime, the distance between the ego and leader vehicles changes by(v_(e)−v_(l))Δt₁=(v_(e)−v_(l))v_(l)/−a_(required).

After the first amount of time, the leader vehicle stops and stays atrest while the ego vehicle continues to brake. A second amount of time(Δt₂=(v_(e)−v_(l))/−a_(required)) passes before the ego vehicle comes toa stop. Over this second amount of time, the distance between thevehicles, d, changes by

${{( {v_{e} - v_{l}} )\Delta\; t_{2}} - {\frac{- a_{required}}{2}\Delta\; t_{2}^{2}}} = {\frac{( {v_{e} - v_{l}} )^{2}}{{- 2}a_{required}}.}$Adding the distance changes together across both the first and secondamounts of time and yields the equation,

${a_{required} = \frac{v_{l}^{2} - v_{e}^{2}}{2( {d - d_{crit}} )}},$discussed above.

It can be appreciated that a_(required) (as a function of the currentstate of the ego and leader vehicles [v_(e); v_(l); d]) increases(approaches 0) whenever a_(ego)<a_(leader) and decreases (approaches−∞)whenever a_(ego)>a_(leader). Furthermore, by applying a controlacceleration, u, equal to a_(crit) as soon as a_(required)=a_(crit), thelongitudinal planning system can guarantee that, at least in atheoretical sense, the ego vehicle will react sufficiently to stop withover the distance d_(crit) even in the worst-case scenario (where theleader vehicle also applies a_(crit) until the leader vehicle stops).Due to ego and leader state measurement and prediction errors, controlinaccuracy, unmodeled dynamics, software latency, etc., the theoreticalsafe following distance may be insufficient to properly ensure collisionavoidance. For this reason, the following distance may be increased toaccount for such variables. Note that this control law,u=a_(ego)=a_(crit) if a_(required)=a_(crit), simultaneously guaranteesthat a_(required) never exceeds a_(crit).

In some embodiments, it may be preferable to not implement anuncomfortable control law that switches discontinuously to this behaviorof u=a_(crit) only when a_(required)=a_(crit). Instead, a blendingfactor that lets u approach a_(required) as a_(required) approachesa_(crit) may be applied by the longitudinal planning system. Such ablending factor may achieve a more conservative and smoother behavior.

Constraints for Input Saturation

In some embodiments, the longitudinal planning system of an ego vehiclemay execute a longitudinal planner which may attempt to control the egovehicle to follow a desired distance behind a leader vehicle given thevelocity of the leader vehicle's speed and to maintain the avoidance ofthe possibility of a collision with the leader vehicle. Such alongitudinal planner should balance both safety and comfort factors.Also, since the possible acceleration and deceleration limits of each ofthe leader and ego vehicles must be a finite amount and cannot beinfinitely large, constraints should be placed on the possibleacceleration and deceleration.

The longitudinal planning system of the ego vehicle may set a criticaldistance for the extreme case where the leader vehicle brakes at itsmaximum deceleration limit and where the leader vehicle continues tobrake at its maximum deceleration limit until it stops. The criticaldistance may be a distance such that the ego vehicle will, in theextreme case, end up at a proper distance from the leader vehicle oncethe leader and ego vehicles are stopped. This is the critical constraintsuch that any state of the ego and leader vehicles should requiremaximum deceleration of the ego vehicle.

The longitudinal planning system must also be capable of dealing withtwo other general scenarios: one where the ego vehicle is at a distancefrom the leader vehicle that is greater than an ideal followingdistance; and the other where the ego vehicle is too close to the leadervehicle given the ideal following distance.

In the scenario where the ego vehicle is further than an ideal distancefrom the leader vehicle and tries to catch up with the leader vehiclefrom some distance greater than the ideal following distance, thelongitudinal planning system may control the ego vehicle and try toconverge with a mild acceleration to a distance range that is furtherthan or equal to the desired following distance, d*, for the steadystate (i.e., vehicles become equalized on speed), any conditions whichviolate this need to apply an upper acceleration limit (or maximumacceleration) to catch up the leader vehicle. Since this distance insteady state may be greater than the desired following distance, inputconstraints associated with this case may be defined as overshootconstraints.

Similarly, in the other scenario where the ego vehicle is too close tothe leader vehicle, the longitudinal planning system of the ego vehicleshould attempt to brake to develop gap for the distance to converge to arange that closer than the desired following distance with a milddeceleration. In the event that the ego vehicle is overly close to theleader vehicle, the longitudinal planning system should require the egovehicle to brake at a deceleration limit for this case. Inputconstraints associated with this case may be defined as undershootconstraints.

Without loss of generality, in classical control theory, when an actualsignal is greater than a reference value, it is called overshoot whichmeans the actual signal has passed the reference value, and vice versa.So, by checking if the current distance, d, of the ego vehicle from theleader vehicle is greater or smaller than the ideal following distance,d*, the condition may be described as overshoot or undershoot,respectively. However, in a longitudinal control scenario, when the egovehicle is approaching to the leader by acceleration, the distanceactually decreases. Once the distance passes the desired distance, i.e.,d<d*, the vehicle may be described as having overshot the desireddistance.

As used herein, the acceleration control input, u, is theacceleration/deceleration of the ego vehicle. The symbol u⁺ may bedefined as accelerations while the symbol u⁻ may be defined asdeceleration, where u⁻<0<u⁺.

Overshoot Constraint

Overshoot constraint may be implemented in the scenario where the egovehicle is at a distance greater than the ideal following distance fromthe leader vehicle and is narrowing the distance from the leader vehicleby traveling with a moderate acceleration. The ego vehicle may beapproaching the leader vehicle from a far distance. The ego vehicle maybe travelling with a mild acceleration and its acceleration input may besaturating at a maximum limit. This maximum acceleration limit, a_(max)⁺, may be considered to be a mild acceleration limit, for examplebetween 1 and 1.5 m/s². As used herein, a superscript plus sign can beinterpreted as meaning acceleration while deceleration which can bedenoted using a superscript minus sign.

In such a scenario, the longitudinal planning system may consider onlythe saturation factors, which may be the overshoot constraint for theacceleration (as opposed to considering any deceleration constraint).For this reason, the longitudinal planning system may need to consideronly the maximum allowed acceleration, u⁺, and not any minimum alloweddeceleration, u⁻. A positive real number d_(o)* that is greater than d*may be defined as a desired distance for the steady state of theovershoot case, wherein steady state indicates that the longitudinalplanning system may focus on the state that the ego vehicle's speedbecomes equalized with the leader vehicle speed. In some embodiments,but not all, the desired distance for the steady state of the overshootcase, d_(o)*, may be set to a greater amount than the desired followingdistance d* for the steady state. Next, a positive real number, d^(b),that is greater than zero may be defined as a threshold for blending theovershoot constraint.

As illustrated in FIG. 9C, the general instructions for an ego vehiclefollowing a leader vehicle may be plotted on a phase portrait. Thevertical axis of the phase portrait illustrates the relative speed ofthe ego vehicle compared to the leader vehicle. For example, pointsabove the speed of 0 m/s reflect states in which the ego vehicle isfaster than the leader vehicle and points below the speed of 0 m/sreflect states in which the ego vehicle is slower than the leadervehicle. The horizontal axis illustrates the relative position of theego vehicle compared to the leader vehicle. For example, points towardthe left side of the horizontal axis reflect states in which the egovehicle is near the leader vehicle while points toward the right side ofthe horizontal axis reflect states in which the ego vehicle isrelatively further from the leader vehicle.

In the example phase portrait illustrated in FIG. 9C, an ideal followingdistance behind the leader vehicle may be set at 8.6 meters. Forexample, the ego vehicle may ideally be travelling at the idealfollowing distance at the same speed as the leader vehicle.

The curved lines on the phase portrait may represent motion plans to befollowed by the ego vehicle. For example, motion plans for states in theupper right-hand portion of the phase portrait may require the egovehicle to move closer to the leader and also to decrease its relativespeed. Similarly, points in the lower righthand portion may require theego vehicle to move closer to the leader vehicle while increasing therelative speed. These motion plan lines represent theoretically idealmotion plans. However, these lines do not take into considerationfactors such as maximum and/or minimum acceleration given comfort and/orvehicle-specific requirements. For example, where the motion plans movein a straight vertical line, the motion plan falls into an impossiblediscontinuity either calling for an unrealistically high or lowacceleration. For this reason, as discussed below, a blending zone maybe created specifying particular rules for the ego vehicle to followwhen near these discontinuities.

Because of these discontinuities, the overshoot scenario may be splitinto three scenarios are considered for the overshoot constraint: (1)standard case, (2) violation case, and (3) blending case.

In the standard case, the actual following distance of the ego vehicleis smaller than the desired overshoot distance by d^(b), i.e.,d−d_(o)*<d^(b). Two cases are considered here:

One, where the ego vehicle is slower than the leader vehicle, i.e.,v_(e)<v_(l). In this scenario, there is still some room to applynon-zero acceleration. In such a scenario, the ego vehicle may set thegoal acceleration according to the following formula:

$u^{+} = {{\max\lbrack {0,{\min\lbrack {\frac{( {v_{e} - v_{\cdot}} )^{2}}{{- 2}( {d - d_{O}^{*}} )},a_{\max}^{+}} \rbrack}} \rbrack}.}$By applying this acceleration and given the situation that the egovehicle is slower, the longitudinal planning system may guarantee thatthe actual distance will converge to d_(o)* as the ego speed, v_(e),approaches v_(l).

Two, where the ego vehicle is faster than or the same speed as theleader vehicle, i.e., v_(e)≥v_(l). Since the actual distance is lessthan the desired overshoot distance and the ego vehicle travels fasterthan the leader vehicle. There is no need to have accelerations, i.e.,throttle needs to be zero if the ego vehicle has throttle/brakeactuation (vs. torque). As a result, the goal acceleration may be set tozero.

In the violation case, the actual following distance is greater than thedesired overshoot distance, i.e. d≥d_(o)*. This means that the overshootconstraint is violated (in the sense of an optimization problemformulation). For example, as illustrated in FIG. 9G, the horizontalportion of the dashed overshoot trajectory on the equal speed line (0 onthe y-axis)) indicates points where the ego vehicle is in the violationcase and travelling at the same speed as the leader vehicle. Points nearbut above the horizontal portion of the dashed overshoot trajectoryrepresent states where the ego vehicle is in the violation casetravelling faster than the ego vehicle and points near but below thehorizontal portion of the dashed overshoot trajectory represent stateswhere the ego vehicle is in the violation case travelling slower thanthe ego vehicle. Depending whether the ego vehicle is slower or fasterthan the leader vehicle, the ego vehicle can choose to accelerate at themild acceleration limit (slower than the leader) or coast withoutapplying an acceleration (faster than the leader). This causes adiscontinuity of the acceleration. This discontinuity is created by amotion plan of a vertical line in states where the ego vehicle isfurther from the leader vehicle than an idea position. Thesediscontinuity states occur at the points where the ego and leadervehicles are travelling at the same speed (on the 0 m/s line of they-axis). Above the 0 m/s line, the ego vehicle is faster than the leadervehicle, so the ego vehicle can coast with no applied acceleration.Below the 0 m/s line, the ego vehicle is slower than the leader vehicle,so the ego vehicle needs to apply a mild acceleration limit. Thiscorresponds to the violation case. Hence, to remove this discontinuity,a blending region is created to blend the acceleration.

In a violation above case, the ego vehicle is faster than the leadervehicle by v^(b), i.e., v_(e)−v_(l)≥v^(b), where v^(b) defines apositive real number as the threshold for blending the overshootconstraint. Since the speed error is above the blending threshold, theactual will converge to the desired overshoot automatically withoutrequiring any acceleration. As a result, the target acceleration may beset to zero.

In a violation below case, the ego vehicle is slower than the leadervehicle. For this case, both the actual distance is far away, and theego speed is slower than the leader vehicle. Hence, the ego vehicleneeds to use maximum allowed acceleration to accelerate and the targetacceleration may be set to a_(max) ⁺.

In a violation blend case, the longitudinal planning system may create ablending region for when the velocity of the ego vehicle exceeds thevelocity of the leader vehicle by a predetermined blending amount,v^(b), (e.g. 5 m/s). When the ego speed reaches and/or exceeds theleader speed to enter this region, the acceleration input may be blendedin this region by a linear interpolation using a formula such as

$u^{+} = {{a_{\max}^{+}( {1 - \frac{v_{e} - v_{l}}{v^{b}}} )}.}$

Since the ego speed is in the region described above,0≤v_(e)−v_(l)<v^(b), the acceleration will gradually go from the mildacceleration limit a_(max) ⁺ to 0 and vice versa.

In a blending case, the actual following distance of the ego vehicle iswithin a desired overshoot distance but does not exceed a threshold forthe blending, i.e., d_(o)*−d^(b)≤d<d_(O)*. When the distance between theego vehicle and the leader vehicle crosses the overshoot constraintline, the gradient of the target acceleration is approaching infinitywhich causes another discontinuity. For example, on the right-hand sideregion of the phase portrait illustrated in FIG. 9C, it is usually thecase that the ego vehicle is too far from the leader vehicle andtravelling at a less than optimal speed. In such a scenario, a_(max) ⁺is applied. As a further example, on the left-hand side region of thephase portrait illustrated in FIG. 9C, it is usually the case that theego vehicle is too closer to the leader vehicle and depending on whetherthe ego vehicle is faster or slower than the leader vehicle, the targetacceleration can be small or zero. For this reason, the longitudinalplanning system may create a blending region on the left-hand side witha width of d^(b) for the blending.

The longitudinal planning system may follow a particular method tocalculate the target acceleration in this region. For the violationcase, the zero acceleration speed difference and maximum accelerationspeed difference are defined at v^(b) and zero, respectively, i.e., whenthe speed difference is at v^(b) the ego vehicle acceleration u⁺=zeroand when the speed difference is at 0 the ego vehicle accelerationu⁺=a_(max) ⁺. However, since the blending case considers the region onthe left-hand side of the overshoot constraint trajectory, thezero-acceleration speed and maximum acceleration speed need to shiftdown based on the distance difference. Define v_(min) ^(b)=−√{squareroot over ((d−d_(O)*)a_(max) ⁺)} the speed difference for the egovehicle at the distance difference db with accelerates a_(max) ⁺. Then,the ego speed and leader speed may be equalized when the distancedifference is 0. Next, the longitudinal planning system may define:

${d_{frac} = {{- \frac{d - d_{O}^{*}}{d^{b}}}\mspace{14mu}{as}\mspace{14mu} a\mspace{14mu}{distance}\mspace{14mu}{difference}\mspace{14mu}{fraction}}};$v_(a 0) = (1 − d_(frac))v^(b)  as  a  zero-acceleration  speed;v_(amax) = d_(frac)v^(b)  as  a  max   acceleration  speed;${v_{frac} = {\frac{v_{e} - v_{l} - v_{a\; 0}}{v_{amax} - v_{a\; 0}}\mspace{14mu}{as}\mspace{14mu} a\mspace{14mu}{speed}\mspace{14mu}{difference}\mspace{14mu}{fraction}}};$${a_{left} = {\frac{( {v_{frac}v_{\min}^{b}} )^{2}}{2*d^{b}}\mspace{14mu}{as}\mspace{14mu}{an}\mspace{14mu}{acceleration}\mspace{14mu}{for}\mspace{14mu}{the}\mspace{14mu}{left}\mspace{14mu}{edge}\mspace{14mu}{of}\mspace{14mu}{the}\mspace{14mu}{blending}\mspace{14mu}{region}}};{and}$a_(right) = v_(frac)a_(max)⁺  as  an  acceleration  for  the  right  edge  of  the  blending  region.

As a result, the blended acceleration may be calculated asu⁺=(1−d_(frac))a_(right)+d_(frac)a_(left).

Undershoot Constraint

Undershoot constraint deals with a scenario where the ego vehicle isdriving near a leader vehicle and when there is still room for the egovehicle to use a deceleration within a mild deceleration limit, forexample −1 m/s², to develop a desired gap from the leader vehicle toreach the critical distance. Such a deceleration limit may be defined asa_(eq) ⁻. When applying undershoot constraints, the longitudinalplanning system may consider only constraint for the deceleration (asopposed to considering acceleration constraints), that is, it only dealswith u⁻.

As illustrated in FIG. 9G, the curved portion of the solid undershootconstraint line represents states where the target acceleration is at orless than the deceleration limit a_(eq) ⁻. There are three scenarios tobe considered for the undershoot constraint scenario: a standard case,where the actual following distance is greater than a desired undershootdistance by a blending distance, d^(b); a violation case, where theactual following distance is less than a desired undershoot distanced_(u)*, and a blending case where the actual following distance isgreater than a desired undershoot distance but does not exceed thethreshold for the blending.

In the standard case, the actual following distance, d, of the egovehicle is greater than the desired undershoot distance, d_(u)*, byd^(b), i.e., d−d_(u)*>d^(b). In the standard case, two cases areconsidered: one, where the ego vehicle is faster than the leadervehicle; and two, where the ego vehicle is slower than the leadervehicle.

In the first of the standard cases, the ego vehicle is faster than theleader vehicle, i.e., v_(e)>v_(l). In this scenario, there is still someroom to apply non-zero deceleration. The longitudinal planning systemshould, in this case, apply a relatively maximum deceleration as:

$u^{-} = {{\max\lbrack {a_{eq}^{-},{\min\lbrack {\frac{- ( {v_{e} - v_{l}} )^{2}}{2( {d - d_{u}^{*}} )},0} \rbrack}} \rbrack}.}$By applying this relatively maximum deceleration and given the situationthat the ego vehicle is faster, the longitudinal planning system mayguarantee that the actual distance between the ego and leader vehicleswill converge to d_(u)* as the ego speed v_(e)→v_(l).

In the second of the standard cases, the ego vehicle is slower than theleader vehicle, i.e., v_(e)≤v_(l). Since the actual distance between theego and leader vehicles is greater than the desired undershoot distanceand the ego vehicle travels slower than the leader vehicle, there is noneed to have deceleration, i.e., the braking factor of the ego vehicleshould be zero if the ego vehicle has throttle/brake actuation (asopposed to torque). In this case, the acceleration of the ego vehicle,u⁺, should be set to zero.

In the violation case, the actual following distance of the ego vehicleis less than the desired undershoot distance, i.e. d≤d_(u)*. This meansthat the undershoot constraint is violated (in the sense of anoptimization problem formulation). When the speed of the ego vehiclecompared to the speed of the leader vehicle crosses the 0 m/s relativespeed line in the vertical direction, the ego speed goes from slower tofaster than the leader speed, or vice versa. Depending whether the egovehicle is slower or faster than the leader vehicle, the ego vehicle canchoose to decelerate at an equalized deceleration limit, a_(eq) ⁻, (whenthe ego vehicle is faster than the leader vehicle) or coast with zerodeceleration (when the ego vehicle is slower than the leader vehicle).This causes a discontinuity of the deceleration. Hence, a blendingregion is created to blend the deceleration to remove the discontinuity.There are three scenarios to consider in the violation case: where theego vehicle is faster than the leader vehicle (violation above case),where the ego vehicle is slower than the leader vehicle by at least ablending velocity factor (violation below case), and where the egovehicle is slower than the leader vehicle by less than the blendingfactor (violation blend case).

In the violation above case, the ego vehicle is faster than the leadervehicle, i.e., v_(e)−v _(l)>0. Since the speed error is above zero, theego vehicle needs to use the maximum allowed deceleration, that isa_(eq) ⁻ to deceleration. As such, the longitudinal planning system mayset the target deceleration to a_(eq) ⁻.

In the violation below case, the ego vehicle is slower than the leadervehicle by v^(b), i.e., v_(e)−v_(l)≤−v^(b). For this case, the egovehicle does not need to apply deceleration and the longitudinalplanning system may set the target deceleration to zero.

In the violation blend case, where the ego vehicle is slower than theleader vehicle by less than the blending factor, the longitudinalplanning system may create a blending region below the horizontalportion of the dashed overshoot constraint line, such as thatillustrated in FIG. 9G, by v^(b). When the ego speed crosses the leaderspeed line to enter this region, the deceleration input is blended inthis region by a linear interpolation. For example, the decelerationinput may be set as follows by the formula:

$u^{-} = {{a_{eq}^{-}( {1 + \frac{v_{e} - v_{l}}{v^{b}}} )}.}$Since the ego speed is in the region described above,−v^(b)<v_(e)−v_(l)≤0. As a result, the deceleration will gradually gofrom the equalized deceleration limit a_(eq) ⁻ to 0 and vice versa.

In the blending case, the actual following distance of the ego vehiclebehind the leader vehicle is greater than the desired undershootdistance but does not exceed a threshold distance, d^(b), for theblending, i.e., d_(u)*<d≤d^(b)+d_(u)*. When the distance between the egovehicle and the leader vehicle crosses the solid undershoot constraintline near the 0 m/s relative speed line, the gradient of the targetacceleration is approaching infinity which causes a discontinuity. Forexample, on the left-hand side region of the phase portrait asillustrated in FIG. 9C, it is usually the case that the ego vehicle istoo close to the leader vehicle and travelling faster than ideal and asa result the equalize deceleration limit a_(eq) ⁻ is applied. On theright-hand side region of the phase portrait illustrated in FIG. 9C, itis usually the case that the ego vehicle does not violate the undershootdistance and, depending on whether the ego vehicle is faster or slowerthan the leader, the target deceleration can be small or zero. Hence,the longitudinal planner system may create a blending region on theright-hand side with a width of d^(b) for the blending. The method tocalculate the deceleration in this region is similar to the overshootcase.

Critical Constraint

The application of critical constraint is similar to the application ofundershoot constraint as discussed above, except that the longitudinalplanning system may consider the possibility of the leader vehiclebraking at the deceleration limit a_(crit) ⁻. The longitudinal planningsystem may deal with the case that if both the ego and leader vehiclesdecelerate at the same deceleration, the ego vehicle should come to astop at a critical distance d_(crit) from the leader vehicle after theleader vehicle has stopped. This critical distance may be less than theundershoot distance and may be considered as the minimum safety boundaryto guarantee collision avoidance. In the critical constraint scenario,there are three cases: a standard case, in which the actual followingdistance of the ego vehicle is greater than the desired criticaldistance by at least a blending threshold amount; a violation case, inwhich the actual following distance of the ego vehicle is less than thecritical distance; and the blending case, in which the actual followingdistance of the ego vehicle is within the critical distance but does notexceed the threshold for the blending.

In the standard case, the actual following distance is greater than thedesired critical distance by d^(b), i.e., d−d_(crit)>d^(b). Two casesare considered in the standard case: one, where the ego vehicle isfaster than the leader vehicle and two, where the ego vehicle is notfaster than the leader vehicle.

In the first case, where the ego vehicle is faster than the leadervehicle, i.e., v_(e)>v_(l), there is still some room to apply non-zerodeceleration. The longitudinal planning system may set the decelerationof the ego vehicle according to the following formula:

$u^{-} = {{\max\lbrack {a_{crit}^{-},{\min\lbrack {\frac{v_{l}^{2} - v_{e}^{2}}{2( {d - d_{crit}} )},0} \rbrack}} \rbrack}.}$By applying this deceleration and given the situation that the egovehicle is faster than the leader vehicle, the ego vehicle may guaranteethat the actual distance will converge to the proper overshoot distance,d_(O)*, as the ego vehicle speed, v_(e), approaches the speed of theleader vehicle, v_(l).

In the second case, where the ego vehicle is slower than or the samespeed as the leader vehicle, i.e., v_(e)≤v_(l); Since the actualdistance is greater than the critical distance, and the ego vehicletravels slower than the leader vehicle, there is no need to have applieddeceleration, i.e., the brake can be zero if the ego vehicle hasthrottle/brake actuation (vs. torque). As such, the target accelerationof the ego vehicle, u⁺, may be set to zero.

In the violation case, the actual following distance of the ego vehicleis less than or equal to the critical distance, i.e. d≤d_(crit). Thismeans that the critical constraint is violated. When the actual speed ofthe ego vehicle crosses the horizontal axis in the vertical direction,the ego speed goes from slower to faster than the leader speed, or viceversa. Depending on whether the ego vehicle is slower or faster than theleader vehicle, the ego vehicle can choose to decelerate at the criticaldeceleration limit (faster than the leader) or coast with zero applieddeceleration (slower than the leader). This causes a discontinuity ofthe deceleration. For this reason, a blending region may be created bythe longitudinal planning system to blend the deceleration to remove thediscontinuity.

In the violation above case, the ego vehicle is faster than the leadervehicle, i.e., v_(e)>v_(l)>0. Since the speed error is above zero, theego vehicle needs to set its deceleration, u⁻, to the maximum alloweddeceleration, a_(crit) ⁻, to decelerate.

In the violation below case, the ego vehicle is slower than the leadervehicle by at least a critical blend case velocity, v^(b), i.e.,v_(e)−v_(l)≤−v_(crit). For this case, the ego vehicle does not need toapply deceleration and the acceleration of the ego vehicle may be set tozero.

In the violation blend case, the ego vehicle may create a blendingregion by v^(b). When the ego speed crosses the leader speed line toenter this region, the deceleration input is blended in this region by alinear interpolation. The longitudinal planning system may in such ascenario set the ego vehicle's deceleration according to the followingformula:

$u^{-} = {{a_{crit}^{-}( {1 + \frac{v_{e} - v_{l}}{v^{b}}} )}.}$As a result, when the ego speed is in the region described above,−v^(b)<v_(e)−v_(l)≤0, the deceleration will gradually go from theequalized deceleration limit a_(eq) ⁻ to 0 and vice versa.

In the blending case, the actual following distance of the ego vehicleis within the critical distance but does not exceed the threshold forthe blending, i.e., d_(crit)<d≤d^(b)+d_(crit). When the distance crossesthe critical constraint line near the 0 m/s relative speed line, thegradient of the target acceleration is approaching infinity which causesanother discontinuity. For instance, on the left-hand side region of thephase portrait as illustrated in FIG. 9C, it is usually the case thatthe ego vehicle is too close to the leader vehicle and travelling fasterthan ideal so that the deceleration a_(crit) ⁻ is applied. On theright-hand side region, it is usually the case that the ego vehicle doesnot violate the undershoot distance and, depending on whether the egovehicle is faster or slower than ideal, the target deceleration can besmall or zero. Hence, the longitudinal planner system may create aregion on the right-hand side of the phase portrait as illustrated inFIG. 9C with a width of d^(b) for the blending. The method to calculatethe deceleration in this region is similar to the overshoot andundershoot cases except that it considers both vehicles braking at thedeceleration limit and the derivation will be skipped.

Blending of the Constraints

Using LQR systems, the longitudinal planning system may be capable ofblending overshoot control as discussed above. The longitudinal planningsystem may set the upper and lower acceleration/deceleration boundariesto blend the overshoot control with LQR. When the overshoot control isat an upper boundary, the longitudinal planning system may utilize fullovershoot control. When the overshoot control is below a lower boundary,the longitudinal planning system may utilize full LQR control. Thelongitudinal planning system may blend its control scheme between thesetwo. After blending the overshoot with LQR control, the longitudinalplanning system may blend the undershoot control in the same way.Similarly, after blending the overshoot and undershoot controls, thelongitudinal planning system may blend the control scheme with criticalcontrol. As a result, the longitudinal planning system may be capable ofpicking a minimum of the blending results and using the minimum as acontrol for distance keeping.

Speed Limit

In some embodiments, the longitudinal planning system may utilize aproportional control based on an error between a speed limit and thespeed of the ego vehicle. Then, the acceleration/deceleration input maybe saturated within a predefined minimum road speed acceleration and amaximum allowed acceleration. After calculating the control necessary totrack the speed limit, the longitudinal planning system may determine aminimum of the speed limit control and the distance keeping control.

As illustrated in FIG. 11 , a method may be performed by a longitudinalplanning system. In some embodiments, a longitudinal planning system maybe a vehicle control system. A longitudinal planning system may comprisea computing system. A longitudinal planning system may comprise aprocessor and memory. A processor of a longitudinal planning system mayperform steps of the method illustrated in FIG. 11 or steps of othermethods.

The method illustrated in FIG. 11 is used as an illustration of apossible embodiment of the present disclosure and should not beconsidered to be limiting in any way.

In some embodiments, a method may begin upon the occurrence of one of anumber of events at step 1103. For example, the method may begin uponthe vehicle starting, the vehicle beginning to move, the vehicledetecting the presence of a leader vehicle while travelling, a user suchas a driver of the vehicle initiating the method, or other events. Themethod may be continuously running while the vehicle is powered on ormay start and stop as needed.

After the method has initiated, the method may comprise the longitudinalplanning system determining a state of the ego vehicle in step 1103.Determining the state of the ego vehicle may comprise readingmeasurements of sensors on or around the vehicle. Such sensors may beactively monitored, or the measurements may be read from memoryaccessible by the longitudinal planning system. The state of the egovehicle may comprise information such as current velocity, currentacceleration, acceleration capabilities of the vehicle, or otherrelative information. In some embodiments, the state of the ego vehiclemay comprise information such as road conditions, speed limits, presenceof other vehicles, or other information.

After, or contemporaneous to, determining the state of the ego vehiclein step 1103, the longitudinal planning system may next determine astate of a leader vehicle in step 1109. Determining the state of theleader vehicle may comprise identifying the leader vehicle. Certainaspects or qualities of the leader vehicle may be determined, such ascurrent velocity of the leader vehicle, current acceleration of theleader vehicle, acceleration capabilities of the leader vehicle, orother information related to the leader vehicle.

Next, the longitudinal planning system may determine a critical distancebased on the state of the ego vehicle and the state of the leadervehicle in step 1112. The critical distance may be determined based on anumber of factors and may be the distance at which the ego vehicle cansafely stop behind the leader vehicle in the scenario at which theleader vehicle immediately begins braking to its maximum capabilities.The critical distance may also be determined based on factors such asroad conditions, leader vehicle type, etc.

Next, the longitudinal planning system may compare the distance betweenthe ego vehicle and the leader vehicle with the critical distance instep 1115. In step 1115 the ego vehicle may also consider the speed ofthe ego vehicle and the speed of the leader vehicle and using the speedsof the vehicles and the distance between the vehicles, the longitudinalplanning system may determine a state of the ego vehicle.

Based on the state of the ego vehicle and the state of the leadervehicle and the determined critical distance, the longitudinal planningsystem may apply one or more constraints in step 1118. These constraintsmay be as described herein and may affect the overall control plan ofthe ego vehicle.

Based on the state of the ego and leader vehicles and based on theapplication of the constraints, the longitudinal planning system maynext determine a target acceleration and/or motion plan 1121 for the egovehicle to follow. In step 1124, the longitudinal planning system of theego vehicle may then control the ego vehicle to track the targetacceleration and/or motion plan 1124. At step 1127, the method may end.

Any of the steps, functions, and operations discussed herein can beperformed continuously and automatically.

The exemplary systems and methods of this disclosure have been describedin relation to vehicle systems and electric vehicles. However, to avoidunnecessarily obscuring the present disclosure, the precedingdescription omits a number of known structures and devices. Thisomission is not to be construed as a limitation of the scope of theclaimed disclosure. Specific details are set forth to provide anunderstanding of the present disclosure. It should, however, beappreciated that the present disclosure may be practiced in a variety ofways beyond the specific detail set forth herein.

Furthermore, while the exemplary embodiments illustrated herein show thevarious components of the system collocated, certain components of thesystem can be located remotely, at distant portions of a distributednetwork, such as a LAN and/or the Internet, or within a dedicatedsystem. Thus, it should be appreciated, that the components of thesystem can be combined into one or more devices, such as a server,communication device, or collocated on a particular node of adistributed network, such as an analog and/or digital telecommunicationsnetwork, a packet-switched network, or a circuit-switched network. Itwill be appreciated from the preceding description, and for reasons ofcomputational efficiency, that the components of the system can bearranged at any location within a distributed network of componentswithout affecting the operation of the system.

Furthermore, it should be appreciated that the various links connectingthe elements can be wired or wireless links, or any combination thereof,or any other known or later developed element(s) that is capable ofsupplying and/or communicating data to and from the connected elements.These wired or wireless links can also be secure links and may becapable of communicating encrypted information. Transmission media usedas links, for example, can be any suitable carrier for electricalsignals, including coaxial cables, copper wire, and fiber optics, andmay take the form of acoustic or light waves, such as those generatedduring radio-wave and infra-red data communications.

While the flowcharts have been discussed and illustrated in relation toa particular sequence of events, it should be appreciated that changes,additions, and omissions to this sequence can occur without materiallyaffecting the operation of the disclosed embodiments, configuration, andaspects.

A number of variations and modifications of the disclosure can be used.It would be possible to provide for some features of the disclosurewithout providing others.

In yet another embodiment, the systems and methods of this disclosurecan be implemented in conjunction with a special purpose computer, aprogrammed microprocessor or microcontroller and peripheral integratedcircuit element(s), an ASIC or other integrated circuit, a digitalsignal processor, a hard-wired electronic or logic circuit such asdiscrete element circuit, a programmable logic device or gate array suchas PLD, PLA, FPGA, PAL, special purpose computer, any comparable means,or the like. In general, any device(s) or means capable of implementingthe methodology illustrated herein can be used to implement the variousaspects of this disclosure. Exemplary hardware that can be used for thepresent disclosure includes computers, handheld devices, telephones(e.g., cellular, Internet enabled, digital, analog, hybrids, andothers), and other hardware known in the art. Some of these devicesinclude processors (e.g., a single or multiple microprocessors), memory,nonvolatile storage, input devices, and output devices. Furthermore,alternative software implementations including, but not limited to,distributed processing or component/object distributed processing,parallel processing, or virtual machine processing can also beconstructed to implement the methods described herein.

In yet another embodiment, the disclosed methods may be readilyimplemented in conjunction with software using object or object-orientedsoftware development environments that provide portable source code thatcan be used on a variety of computer or workstation platforms.Alternatively, the disclosed system may be implemented partially orfully in hardware using standard logic circuits or VLSI design. Whethersoftware or hardware is used to implement the systems in accordance withthis disclosure is dependent on the speed and/or efficiency requirementsof the system, the particular function, and the particular software orhardware systems or microprocessor or microcomputer systems beingutilized.

In yet another embodiment, the disclosed methods may be partiallyimplemented in software that can be stored on a storage medium, executedon programmed general-purpose computer with the cooperation of acontroller and memory, a special purpose computer, a microprocessor, orthe like. In these instances, the systems and methods of this disclosurecan be implemented as a program embedded on a personal computer such asan applet, JAVA® or CGI script, as a resource residing on a server orcomputer workstation, as a routine embedded in a dedicated measurementsystem, system component, or the like. The system can also beimplemented by physically incorporating the system and/or method into asoftware and/or hardware system.

Although the present disclosure describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Other similar standards and protocols not mentioned hereinare in existence and are considered to be included in the presentdisclosure. Moreover, the standards and protocols mentioned herein, andother similar standards and protocols not mentioned herein areperiodically superseded by faster or more effective equivalents havingessentially the same functions. Such replacement standards and protocolshaving the same functions are considered equivalents included in thepresent disclosure.

The present disclosure, in various embodiments, configurations, andaspects, includes components, methods, processes, systems and/orapparatus substantially as depicted and described herein, includingvarious embodiments, sub combinations, and subsets thereof. Those ofskill in the art will understand how to make and use the systems andmethods disclosed herein after understanding the present disclosure. Thepresent disclosure, in various embodiments, configurations, and aspects,includes providing devices and processes in the absence of items notdepicted and/or described herein or in various embodiments,configurations, or aspects hereof, including in the absence of suchitems as may have been used in previous devices or processes, e.g., forimproving performance, achieving ease, and/or reducing cost ofimplementation.

The foregoing discussion of the disclosure has been presented forpurposes of illustration and description. The foregoing is not intendedto limit the disclosure to the form or forms disclosed herein. In theforegoing Detailed Description for example, various features of thedisclosure are grouped together in one or more embodiments,configurations, or aspects for the purpose of streamlining thedisclosure. The features of the embodiments, configurations, or aspectsof the disclosure may be combined in alternate embodiments,configurations, or aspects other than those discussed above. This methodof disclosure is not to be interpreted as reflecting an intention thatthe claimed disclosure requires more features than are expressly recitedin each claim. Rather, as the following claims reflect, inventiveaspects lie in less than all features of a single foregoing disclosedembodiment, configuration, or aspect. Thus, the following claims arehereby incorporated into this Detailed Description, with each claimstanding on its own as a separate preferred embodiment of thedisclosure.

Moreover, though the description of the disclosure has includeddescription of one or more embodiments, configurations, or aspects andcertain variations and modifications, other variations, combinations,and modifications are within the scope of the disclosure, e.g., as maybe within the skill and knowledge of those in the art, afterunderstanding the present disclosure. It is intended to obtain rights,which include alternative embodiments, configurations, or aspects to theextent permitted, including alternate, interchangeable and/or equivalentstructures, functions, ranges, or steps to those claimed, whether or notsuch alternate, interchangeable and/or equivalent structures, functions,ranges, or steps are disclosed herein, and without intending to publiclydedicate any patentable subject matter.

Embodiments include a method for controlling a vehicle, the methodcomprising: determining, by a processor of a longitudinal planningsystem of the vehicle, a state of the vehicle; determining, by theprocessor, a state of a leader vehicle; based on the determined state ofthe vehicle and the determined state of the leader vehicle, determining,by the processor, a critical distance for the vehicle; comparing, by theprocessor, a distance between the vehicle and the leader vehicle withthe critical distance; based on the comparison, determining, by theprocessor, whether the vehicle is too close to or too far from theleader vehicle; based on the determination, applying, by the processor,one or more of overshoot constraints, undershoot constraints, andcritical constraints; after applying the one or more of overshootconstraints, undershoot constraints, and critical constraints,determining, by the processor, a target acceleration for the vehicle;and controlling, by the longitudinal planning system, the vehicle totrack the target acceleration for the vehicle.

Aspects of the above method include wherein applying, by the processor,one or more of overshoot constraints, undershoot constraints, andcritical constraints comprises applying each of overshoot constraints,undershoot constraints, and critical constraints.

Aspects of the above method include wherein applying each of overshootconstraints, undershoot constraints, and critical constraints comprisesblending the overshoot constraints, undershoot constraints, and criticalconstraints.

Aspects of the above method include wherein overshoot constraintsaccount for standard, violation, and blending cases.

Aspects of the above method include wherein undershoot constraintsaccount for standard, violation, and blending cases.

Aspects of the above method include wherein determining the state of thevehicle comprises determining a velocity of the vehicle.

Aspects of the above method include wherein determining the state of theleader vehicle comprises determining a velocity of the leader vehicleand the distance between the vehicle and the leader vehicle.

Embodiments include a longitudinal planning system for controlling avehicle, the longitudinal planning system comprising: a processor; and acomputer-readable storage medium storing computer-readable instructionswhich, when executed by the processor, cause the processor to performoperations comprising: determining a state of the vehicle; determining astate of a leader vehicle; based on the determined state of the vehicleand the determined state of the leader vehicle, determining a criticaldistance for the vehicle; comparing a distance between the vehicle andthe leader vehicle with the critical distance; based on the comparison,determining whether the vehicle is too close to or too far from theleader vehicle; based on the determination, applying one or more ofovershoot constraints, undershoot constraints, and critical constraints;after applying the one or more of overshoot constraints, undershootconstraints, and critical constraints, determining a target accelerationfor the vehicle; and controlling the vehicle to track the targetacceleration for the vehicle.

Aspects of the above the longitudinal planning system include whereinapplying one or more of overshoot constraints, undershoot constraints,and critical constraints comprises applying each of overshootconstraints, undershoot constraints, and critical constraints.

Aspects of the above the longitudinal planning system include whereinapplying each of overshoot constraints, undershoot constraints, andcritical constraints comprises blending the overshoot constraints,undershoot constraints, and critical constraints.

Aspects of the above the longitudinal planning system include whereinovershoot constraints account for standard, violation, and blendingcases.

Aspects of the above the longitudinal planning system include whereinundershoot constraints account for standard, violation, and blendingcases.

Aspects of the above the longitudinal planning system include whereindetermining the state of the vehicle comprises determining a velocity ofthe vehicle.

Aspects of the above the longitudinal planning system include whereindetermining the state of the leader vehicle comprises determining avelocity of the leader vehicle and the distance between the vehicle andthe leader vehicle.

Embodiments include a computer program product for controlling avehicle, the computer program product comprising: a non-transitorycomputer-readable storage medium having computer-readable program codeembodied therewith, the computer-readable program code configured whenexecuted by a processor of a longitudinal planning system of the vehicleto: determine a state of the vehicle; determine a state of a leadervehicle; based on the determined state of the vehicle and the determinedstate of the leader vehicle, determine a critical distance for thevehicle; compare a distance between the vehicle and the leader vehiclewith the critical distance; based on the comparison, determine whetherthe vehicle is too close to or too far from the leader vehicle; based onthe determination, apply one or more of overshoot constraints,undershoot constraints, and critical constraints; after applying the oneor more of overshoot constraints, undershoot constraints, and criticalconstraints, determine a target acceleration for the vehicle; andcontrol the vehicle to track the target acceleration for the vehicle.

Aspects of the above the computer program product include whereinapplying, by the processor, one or more of overshoot constraints,undershoot constraints, and critical constraints comprises applying eachof overshoot constraints, undershoot constraints, and criticalconstraints.

Aspects of the above the computer program product include whereinapplying each of overshoot constraints, undershoot constraints, andcritical constraints comprises blending the overshoot constraints,undershoot constraints, and critical constraints.

Aspects of the above the computer program product include whereinovershoot constraints account for standard, violation, and blendingcases.

Aspects of the above the computer program product include whereinundershoot constraints account for standard, violation, and blendingcases.

Aspects of the above the computer program product include whereindetermining the state of the leader vehicle comprises determining avelocity of the leader vehicle and the distance between the vehicle andthe leader vehicle.

Any one or more of the aspects/embodiments as substantially disclosedherein.

Any one or more of the aspects/embodiments as substantially disclosedherein optionally in combination with any one or more otheraspects/embodiments as substantially disclosed herein.

One or more means adapted to perform any one or more of the aboveaspects/embodiments as substantially disclosed herein.

The phrases “at least one,” “one or more,” “or,” and “and/or” areopen-ended expressions that are both conjunctive and disjunctive inoperation. For example, each of the expressions “at least one of A, Band C,” “at least one of A, B, or C,” “one or more of A, B, and C,” “oneor more of A, B, or C,” “A, B, and/or C,” and “A, B, or C” means Aalone, B alone, C alone, A and B together, A and C together, B and Ctogether, or A, B and C together.

The term “a” or “an” entity refers to one or more of that entity. Assuch, the terms “a” (or “an”), “one or more,” and “at least one” can beused interchangeably herein. It is also to be noted that the terms“comprising,” “including,” and “having” can be used interchangeably.

The term “automatic” and variations thereof, as used herein, refers toany process or operation, which is typically continuous orsemi-continuous, done without material human input when the process oroperation is performed. However, a process or operation can beautomatic, even though performance of the process or operation usesmaterial or immaterial human input, if the input is received beforeperformance of the process or operation. Human input is deemed to bematerial if such input influences how the process or operation will beperformed. Human input that consents to the performance of the processor operation is not deemed to be “material.”

Aspects of the present disclosure may take the form of an embodimentthat is entirely hardware, an embodiment that is entirely software(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module,” or “system.”Any combination of one or more computer-readable medium(s) may beutilized. The computer-readable medium may be a computer-readable signalmedium or a computer-readable storage medium.

A computer-readable storage medium may be, for example, but not limitedto, an electronic, magnetic, optical, electromagnetic, infrared, orsemiconductor system, apparatus, or device, or any suitable combinationof the foregoing. More specific examples (a non-exhaustive list) of thecomputer-readable storage medium would include the following: anelectrical connection having one or more wires, a portable computerdiskette, a hard disk, a random-access memory (RAM), a read-only memory(ROM), an erasable programmable read-only memory (EPROM or Flashmemory), an optical fiber, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer-readable storage medium may be any tangible medium that cancontain or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer-readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer-readable medium may be transmitted using anyappropriate medium, including, but not limited to, wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

The terms “determine,” “calculate,” “compute,” and variations thereof,as used herein, are used interchangeably and include any type ofmethodology, process, mathematical operation or technique.

The term “electric vehicle” (EV), also referred to herein as an electricdrive vehicle, may use one or more electric motors or traction motorsfor propulsion. An electric vehicle may be powered through a collectorsystem by electricity from off-vehicle sources or may be self-containedwith a battery or generator to convert fuel to electricity. An electricvehicle generally includes a rechargeable electricity storage system(RESS) (also called Full Electric Vehicles (FEV)). Power storage methodsmay include chemical energy stored on the vehicle in on-board batteries(e.g., battery electric vehicle or BEV), on board kinetic energy storage(e.g., flywheels), and/or static energy (e.g., by on-board double-layercapacitors). Batteries, electric double-layer capacitors, and flywheelenergy storage may be forms of rechargeable on-board electrical storage.

The term “hybrid electric vehicle” refers to a vehicle that may combinea conventional (usually fossil fuel-powered) powertrain with some formof electric propulsion. Most hybrid electric vehicles combine aconventional internal combustion engine (ICE) propulsion system with anelectric propulsion system (hybrid vehicle drivetrain). In parallelhybrids, the ICE and the electric motor are both connected to themechanical transmission and can simultaneously transmit power to drivethe wheels, usually through a conventional transmission. In serieshybrids, only the electric motor drives the drivetrain, and a smallerICE works as a generator to power the electric motor or to recharge thebatteries. Power-split hybrids combine series and parallelcharacteristics. A full hybrid, sometimes also called a strong hybrid,is a vehicle that can run on just the engine, just the batteries, or acombination of both. A mid hybrid is a vehicle that cannot be drivensolely on its electric motor, because the electric motor does not haveenough power to propel the vehicle on its own.

The term “rechargeable electric vehicle” or “REV” refers to a vehiclewith on board rechargeable energy storage, including electric vehiclesand hybrid electric vehicles.

What is claimed is:
 1. A method for controlling a vehicle, the methodcomprising: determining, by a processor of a longitudinal planningsystem of the vehicle, a state of the vehicle; determining, by theprocessor, a state of a leader vehicle; based on the determined state ofthe vehicle and the determined state of the leader vehicle, determining,by the processor, a critical distance for the vehicle; comparing, by theprocessor, a distance between the vehicle and the leader vehicle withthe critical distance; based on the comparison, determining, by theprocessor, whether the vehicle is one of too close to the leader vehicleand too far from the leader vehicle; based on determining whether thevehicle is one of too close to the leader vehicle and too far from theleader vehicle, determining, by the processor, one of a targetacceleration and a target deceleration for the vehicle by applying oneor more of an overshoot constraint, an undershoot constraint, and acritical constraint, wherein one or more of the overshoot constraint andthe undershoot constraint accounts for standard, violation, and blendingcases; and controlling, by the processor, the vehicle to track the oneof the target acceleration and the target deceleration for the vehicle.2. The method of claim 1, wherein applying, by the processor, the one ormore of the overshoot constraint, the undershoot constraint, and thecritical constraint comprises applying two or more of the overshootconstraint, the undershoot constraint, and the critical constraint. 3.The method of claim 2, wherein applying two or more of the overshootconstraint, the undershoot constraint, and the critical constraintcomprises blending the two or more of the overshoot constraint, theundershoot constraint, and the critical constraint.
 4. The method ofclaim 1, wherein the overshoot constraint accounts for standard,violation, and blending cases.
 5. The method of claim 1, wherein theundershoot constraint accounts for standard, violation, and blendingcases.
 6. The method of claim 1, wherein determining the state of thevehicle comprises determining a velocity of the vehicle.
 7. The methodof claim 1, wherein determining the state of the leader vehiclecomprises determining a velocity of the leader vehicle and the distancebetween the vehicle and the leader vehicle.
 8. A longitudinal planningsystem for controlling a vehicle, the longitudinal planning systemcomprising: a processor; and a computer-readable storage medium storingcomputer-readable instructions which, when executed by the processor,cause the processor to perform operations comprising: determining astate of the vehicle; determining a state of a leader vehicle; based onthe determined state of the vehicle and the determined state of theleader vehicle, determining a critical distance for the vehicle;comparing a distance between the vehicle and the leader vehicle with thecritical distance; based on the comparison, determining whether thevehicle is one of too close to the leader vehicle and too far from theleader vehicle; based on determining whether the vehicle is one of tooclose to the leader vehicle and too far from the leader vehicle,determining one of a target acceleration for the vehicle and a targetdeceleration for the vehicle by applying one or more of an overshootconstraint, an undershoot constraint, and a critical constraint, whereinone or more of the overshoot constraint and the undershoot constraintaccounts for standard, violation, and blending cases; and controllingthe vehicle to track the one of the target acceleration for the vehicleand the target deceleration for the vehicle.
 9. The longitudinalplanning system of claim 8, wherein applying the one or more of theovershoot constraint, the undershoot constraint, and the criticalconstraint comprises applying two or more of the overshoot constraint,the undershoot constraint, and the critical constraint.
 10. Thelongitudinal planning system of claim 9, wherein applying two or more ofthe overshoot constraint, the undershoot constraint, and the criticalconstraint comprises blending the two or more of the overshootconstraint, the undershoot constraint, and the critical constraint. 11.The longitudinal planning system of claim 8, wherein the overshootconstraint accounts for standard, violation, and blending cases.
 12. Thelongitudinal planning system of claim 8, wherein the undershootconstraint accounts for standard, violation, and blending cases.
 13. Thelongitudinal planning system of claim 8, wherein determining the stateof the vehicle comprises determining a velocity of the vehicle.
 14. Thelongitudinal planning system of claim 8, wherein determining the stateof the leader vehicle comprises determining a velocity of the leadervehicle and the distance between the vehicle and the leader vehicle. 15.A computer program product for controlling a vehicle, the computerprogram product comprising: a non-transitory computer-readable storagemedium having computer-readable program code embodied therewith, thecomputer-readable program code configured when executed by a processorof a longitudinal planning system of the vehicle to: determine a stateof the vehicle; determine a state of a leader vehicle; based on thedetermined state of the vehicle and the determined state of the leadervehicle, determine a critical distance for the vehicle; compare adistance between the vehicle and the leader vehicle with the criticaldistance; based on the comparison, determine whether the vehicle is oneof too close to the leader vehicle and too far from the leader vehicle;based on determining whether the vehicle is one of too close to theleader vehicle and too far from the leader vehicle, determine one of atarget acceleration for the vehicle and a target deceleration for thevehicle by applying one or more of an overshoot constraint, anundershoot constraint, and a critical constraint, wherein one or more ofthe overshoot constraint and the undershoot constraint accounts forstandard, violation, and blending cases; and control the vehicle totrack the one of the target acceleration for the vehicle and the targetdeceleration for the vehicle.
 16. The computer program product of claim15, wherein applying, by the processor, the one or more of the overshootconstraint, the undershoot constraint, and the critical constraintcomprises applying two or more of the overshoot constraint, theundershoot constraint, and the critical constraint.
 17. The computerprogram product of claim 16, wherein applying the two or more of theovershoot constraint, the undershoot constraint, and the criticalconstraint comprises blending the two or more of the overshootconstraint, the undershoot constraint, and the critical constraint. 18.The computer program product of claim 15, wherein the overshootconstraint accounts for standard, violation, and blending cases.
 19. Thecomputer program product of claim 15, wherein the undershoot constraintaccounts for standard, violation, and blending cases.
 20. The computerprogram product of claim 15, wherein determining the state of the leadervehicle comprises determining a velocity of the leader vehicle and thedistance between the vehicle and the leader vehicle.